diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..52595f7a8cd931b98a5cfa44ba7516eef8618b9e --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/stable_video_diffusion/test_stable_video_diffusion.py @@ -0,0 +1,561 @@ +import gc +import random +import tempfile +import unittest + +import numpy as np +import torch +from transformers import ( + CLIPImageProcessor, + CLIPVisionConfig, + CLIPVisionModelWithProjection, +) + +import diffusers +from diffusers import ( + AutoencoderKLTemporalDecoder, + EulerDiscreteScheduler, + StableVideoDiffusionPipeline, + UNetSpatioTemporalConditionModel, +) +from diffusers.utils import load_image, logging +from diffusers.utils.import_utils import is_xformers_available + +from ...testing_utils import ( + CaptureLogger, + backend_empty_cache, + enable_full_determinism, + floats_tensor, + numpy_cosine_similarity_distance, + require_accelerate_version_greater, + require_accelerator, + require_torch_accelerator, + slow, + torch_device, +) +from ..test_pipelines_common import PipelineTesterMixin + + +enable_full_determinism() + + +def to_np(tensor): + if isinstance(tensor, torch.Tensor): + tensor = tensor.detach().cpu().numpy() + + return tensor + + +class StableVideoDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = StableVideoDiffusionPipeline + params = frozenset(["image"]) + batch_params = frozenset(["image", "generator"]) + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + ] + ) + + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + unet = UNetSpatioTemporalConditionModel( + block_out_channels=(32, 64), + layers_per_block=2, + sample_size=32, + in_channels=8, + out_channels=4, + down_block_types=( + "CrossAttnDownBlockSpatioTemporal", + "DownBlockSpatioTemporal", + ), + up_block_types=("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal"), + cross_attention_dim=32, + num_attention_heads=8, + projection_class_embeddings_input_dim=96, + addition_time_embed_dim=32, + ) + scheduler = EulerDiscreteScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + interpolation_type="linear", + num_train_timesteps=1000, + prediction_type="v_prediction", + sigma_max=700.0, + sigma_min=0.002, + steps_offset=1, + timestep_spacing="leading", + timestep_type="continuous", + trained_betas=None, + use_karras_sigmas=True, + ) + + torch.manual_seed(0) + vae = AutoencoderKLTemporalDecoder( + block_out_channels=[32, 64], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + latent_channels=4, + ) + + torch.manual_seed(0) + config = CLIPVisionConfig( + hidden_size=32, + projection_dim=32, + num_hidden_layers=5, + num_attention_heads=4, + image_size=32, + intermediate_size=37, + patch_size=1, + ) + image_encoder = CLIPVisionModelWithProjection(config) + + torch.manual_seed(0) + feature_extractor = CLIPImageProcessor(crop_size=32, size=32) + components = { + "unet": unet, + "image_encoder": image_encoder, + "scheduler": scheduler, + "vae": vae, + "feature_extractor": feature_extractor, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device="cpu").manual_seed(seed) + + image = floats_tensor((1, 3, 32, 32), rng=random.Random(0)).to(device) + inputs = { + "generator": generator, + "image": image, + "num_inference_steps": 2, + "output_type": "pt", + "min_guidance_scale": 1.0, + "max_guidance_scale": 2.5, + "num_frames": 2, + "height": 32, + "width": 32, + } + return inputs + + @unittest.skip("Deprecated functionality") + def test_attention_slicing_forward_pass(self): + pass + + @unittest.skip("Batched inference works and outputs look correct, but the test is failing") + def test_inference_batch_single_identical( + self, + batch_size=2, + expected_max_diff=1e-4, + ): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for components in pipe.components.values(): + if hasattr(components, "set_default_attn_processor"): + components.set_default_attn_processor() + pipe.to(torch_device) + + pipe.set_progress_bar_config(disable=None) + inputs = self.get_dummy_inputs(torch_device) + + # Reset generator in case it is has been used in self.get_dummy_inputs + inputs["generator"] = torch.Generator("cpu").manual_seed(0) + + logger = logging.get_logger(pipe.__module__) + logger.setLevel(level=diffusers.logging.FATAL) + + # batchify inputs + batched_inputs = {} + batched_inputs.update(inputs) + + batched_inputs["generator"] = [torch.Generator("cpu").manual_seed(0) for i in range(batch_size)] + batched_inputs["image"] = torch.cat([inputs["image"]] * batch_size, dim=0) + + output = pipe(**inputs).frames + output_batch = pipe(**batched_inputs).frames + + assert len(output_batch) == batch_size + + max_diff = np.abs(to_np(output_batch[0]) - to_np(output[0])).max() + assert max_diff < expected_max_diff + + @unittest.skip("Test is similar to test_inference_batch_single_identical") + def test_inference_batch_consistent(self): + pass + + def test_np_output_type(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + inputs["output_type"] = "np" + output = pipe(**inputs).frames + self.assertTrue(isinstance(output, np.ndarray)) + self.assertEqual(len(output.shape), 5) + + def test_dict_tuple_outputs_equivalent(self, expected_max_difference=1e-4): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + output = pipe(**self.get_dummy_inputs(generator_device)).frames[0] + output_tuple = pipe(**self.get_dummy_inputs(generator_device), return_dict=False)[0] + + max_diff = np.abs(to_np(output) - to_np(output_tuple)).max() + self.assertLess(max_diff, expected_max_difference) + + @unittest.skip("Test is currently failing") + def test_float16_inference(self, expected_max_diff=5e-2): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + components = self.get_dummy_components() + pipe_fp16 = self.pipeline_class(**components) + for component in pipe_fp16.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe_fp16.to(torch_device, torch.float16) + pipe_fp16.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs).frames[0] + + fp16_inputs = self.get_dummy_inputs(torch_device) + output_fp16 = pipe_fp16(**fp16_inputs).frames[0] + + max_diff = np.abs(to_np(output) - to_np(output_fp16)).max() + self.assertLess(max_diff, expected_max_diff, "The outputs of the fp16 and fp32 pipelines are too different.") + + @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU") + @require_accelerator + def test_save_load_float16(self, expected_max_diff=1e-2): + components = self.get_dummy_components() + for name, module in components.items(): + if hasattr(module, "half"): + components[name] = module.to(torch_device).half() + + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs).frames[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for name, component in pipe_loaded.components.items(): + if hasattr(component, "dtype"): + self.assertTrue( + component.dtype == torch.float16, + f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.", + ) + + inputs = self.get_dummy_inputs(torch_device) + output_loaded = pipe_loaded(**inputs).frames[0] + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess( + max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading." + ) + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + if not hasattr(self.pipeline_class, "_optional_components"): + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + # set all optional components to None + for optional_component in pipe._optional_components: + setattr(pipe, optional_component, None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + output = pipe(**inputs).frames[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for optional_component in pipe._optional_components: + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + output_loaded = pipe_loaded(**inputs).frames[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, expected_max_difference) + + def test_save_load_local(self, expected_max_difference=9e-4): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs).frames[0] + + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + logger.setLevel(diffusers.logging.INFO) + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + + with CaptureLogger(logger) as cap_logger: + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + + for name in pipe_loaded.components.keys(): + if name not in pipe_loaded._optional_components: + assert name in str(cap_logger) + + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output_loaded = pipe_loaded(**inputs).frames[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, expected_max_difference) + + @require_accelerator + def test_to_device(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.set_progress_bar_config(disable=None) + + pipe.to("cpu") + model_devices = [ + component.device.type for component in pipe.components.values() if hasattr(component, "device") + ] + self.assertTrue(all(device == "cpu" for device in model_devices)) + + output_cpu = pipe(**self.get_dummy_inputs("cpu")).frames[0] + self.assertTrue(np.isnan(output_cpu).sum() == 0) + + pipe.to(torch_device) + model_devices = [ + component.device.type for component in pipe.components.values() if hasattr(component, "device") + ] + self.assertTrue(all(device == torch_device for device in model_devices)) + + output_device = pipe(**self.get_dummy_inputs(torch_device)).frames[0] + self.assertTrue(np.isnan(to_np(output_device)).sum() == 0) + + def test_to_dtype(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.set_progress_bar_config(disable=None) + + model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")] + self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes)) + + pipe.to(dtype=torch.float16) + model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")] + self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes)) + + @require_accelerator + @require_accelerate_version_greater("0.14.0") + def test_sequential_cpu_offload_forward_pass(self, expected_max_diff=1e-4): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + output_without_offload = pipe(**inputs).frames[0] + + pipe.enable_sequential_cpu_offload(device=torch_device) + + inputs = self.get_dummy_inputs(generator_device) + output_with_offload = pipe(**inputs).frames[0] + + max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() + self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results") + + @require_accelerator + @require_accelerate_version_greater("0.17.0") + def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4): + generator_device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(generator_device) + output_without_offload = pipe(**inputs).frames[0] + + pipe.enable_model_cpu_offload(device=torch_device) + inputs = self.get_dummy_inputs(generator_device) + output_with_offload = pipe(**inputs).frames[0] + + max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() + self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results") + offloaded_modules = [ + v + for k, v in pipe.components.items() + if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload + ] + ( + self.assertTrue(all(v.device.type == "cpu" for v in offloaded_modules)), + f"Not offloaded: {[v for v in offloaded_modules if v.device.type != 'cpu']}", + ) + + @unittest.skipIf( + torch_device != "cuda" or not is_xformers_available(), + reason="XFormers attention is only available with CUDA and `xformers` installed", + ) + def test_xformers_attention_forwardGenerator_pass(self): + expected_max_diff = 9e-4 + + if not self.test_xformers_attention: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output_without_offload = pipe(**inputs).frames[0] + output_without_offload = ( + output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload + ) + + pipe.enable_xformers_memory_efficient_attention() + inputs = self.get_dummy_inputs(torch_device) + output_with_offload = pipe(**inputs).frames[0] + output_with_offload = ( + output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload + ) + + max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() + self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results") + + def test_disable_cfg(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + inputs["max_guidance_scale"] = 1.0 + output = pipe(**inputs).frames + self.assertEqual(len(output.shape), 5) + + +@slow +@require_torch_accelerator +class StableVideoDiffusionPipelineSlowTests(unittest.TestCase): + def setUp(self): + # clean up the VRAM before each test + super().setUp() + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + # clean up the VRAM after each test + super().tearDown() + gc.collect() + backend_empty_cache(torch_device) + + def test_sd_video(self): + pipe = StableVideoDiffusionPipeline.from_pretrained( + "stabilityai/stable-video-diffusion-img2vid", + variant="fp16", + torch_dtype=torch.float16, + ) + pipe.enable_model_cpu_offload(device=torch_device) + pipe.set_progress_bar_config(disable=None) + image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true" + ) + + generator = torch.Generator("cpu").manual_seed(0) + num_frames = 3 + + output = pipe( + image=image, + num_frames=num_frames, + generator=generator, + num_inference_steps=3, + output_type="np", + ) + + image = output.frames[0] + assert image.shape == (num_frames, 576, 1024, 3) + + image_slice = image[0, -3:, -3:, -1] + expected_slice = np.array([0.8592, 0.8645, 0.8499, 0.8722, 0.8769, 0.8421, 0.8557, 0.8528, 0.8285]) + assert numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice.flatten()) < 1e-3 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipeline_utils.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipeline_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6d9e681979762239bb59fce967bb5bb5462a8d79 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipeline_utils.py @@ -0,0 +1,952 @@ +import contextlib +import io +import re +import unittest + +import torch +from PIL import Image +from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer + +from diffusers import ( + AnimateDiffPipeline, + AnimateDiffVideoToVideoPipeline, + AutoencoderKL, + DDIMScheduler, + MotionAdapter, + StableDiffusionImg2ImgPipeline, + StableDiffusionInpaintPipeline, + StableDiffusionPipeline, + UNet2DConditionModel, +) +from diffusers.pipelines.pipeline_loading_utils import is_safetensors_compatible, variant_compatible_siblings + +from ..testing_utils import require_torch_accelerator, torch_device + + +class IsSafetensorsCompatibleTests(unittest.TestCase): + def test_all_is_compatible(self): + filenames = [ + "safety_checker/pytorch_model.bin", + "safety_checker/model.safetensors", + "vae/diffusion_pytorch_model.bin", + "vae/diffusion_pytorch_model.safetensors", + "text_encoder/pytorch_model.bin", + "text_encoder/model.safetensors", + "unet/diffusion_pytorch_model.bin", + "unet/diffusion_pytorch_model.safetensors", + ] + self.assertTrue(is_safetensors_compatible(filenames)) + + def test_diffusers_model_is_compatible(self): + filenames = [ + "unet/diffusion_pytorch_model.bin", + "unet/diffusion_pytorch_model.safetensors", + ] + self.assertTrue(is_safetensors_compatible(filenames)) + + def test_diffusers_model_is_not_compatible(self): + filenames = [ + "safety_checker/pytorch_model.bin", + "safety_checker/model.safetensors", + "vae/diffusion_pytorch_model.bin", + "vae/diffusion_pytorch_model.safetensors", + "text_encoder/pytorch_model.bin", + "text_encoder/model.safetensors", + "unet/diffusion_pytorch_model.bin", + # Removed: 'unet/diffusion_pytorch_model.safetensors', + ] + self.assertFalse(is_safetensors_compatible(filenames)) + + def test_transformer_model_is_compatible(self): + filenames = [ + "text_encoder/pytorch_model.bin", + "text_encoder/model.safetensors", + ] + self.assertTrue(is_safetensors_compatible(filenames)) + + def test_transformer_model_is_not_compatible(self): + filenames = [ + "safety_checker/pytorch_model.bin", + "safety_checker/model.safetensors", + "vae/diffusion_pytorch_model.bin", + "vae/diffusion_pytorch_model.safetensors", + "text_encoder/pytorch_model.bin", + # Removed: 'text_encoder/model.safetensors', + "unet/diffusion_pytorch_model.bin", + "unet/diffusion_pytorch_model.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + + def test_all_is_compatible_variant(self): + filenames = [ + "safety_checker/pytorch_model.fp16.bin", + "safety_checker/model.fp16.safetensors", + "vae/diffusion_pytorch_model.fp16.bin", + "vae/diffusion_pytorch_model.fp16.safetensors", + "text_encoder/pytorch_model.fp16.bin", + "text_encoder/model.fp16.safetensors", + "unet/diffusion_pytorch_model.fp16.bin", + "unet/diffusion_pytorch_model.fp16.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + self.assertTrue(is_safetensors_compatible(filenames, variant="fp16")) + + def test_diffusers_model_is_compatible_variant(self): + filenames = [ + "unet/diffusion_pytorch_model.fp16.bin", + "unet/diffusion_pytorch_model.fp16.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + self.assertTrue(is_safetensors_compatible(filenames, variant="fp16")) + + def test_diffusers_model_is_compatible_variant_mixed(self): + filenames = [ + "unet/diffusion_pytorch_model.bin", + "unet/diffusion_pytorch_model.fp16.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + self.assertTrue(is_safetensors_compatible(filenames, variant="fp16")) + + def test_diffusers_model_is_not_compatible_variant(self): + filenames = [ + "safety_checker/pytorch_model.fp16.bin", + "safety_checker/model.fp16.safetensors", + "vae/diffusion_pytorch_model.fp16.bin", + "vae/diffusion_pytorch_model.fp16.safetensors", + "text_encoder/pytorch_model.fp16.bin", + "text_encoder/model.fp16.safetensors", + "unet/diffusion_pytorch_model.fp16.bin", + # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors', + ] + self.assertFalse(is_safetensors_compatible(filenames)) + + def test_transformer_model_is_compatible_variant(self): + filenames = [ + "text_encoder/pytorch_model.fp16.bin", + "text_encoder/model.fp16.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + self.assertTrue(is_safetensors_compatible(filenames, variant="fp16")) + + def test_transformer_model_is_not_compatible_variant(self): + filenames = [ + "safety_checker/pytorch_model.fp16.bin", + "safety_checker/model.fp16.safetensors", + "vae/diffusion_pytorch_model.fp16.bin", + "vae/diffusion_pytorch_model.fp16.safetensors", + "text_encoder/pytorch_model.fp16.bin", + "unet/diffusion_pytorch_model.fp16.bin", + "unet/diffusion_pytorch_model.fp16.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + + def test_transformer_model_is_compatible_variant_extra_folder(self): + filenames = [ + "safety_checker/pytorch_model.fp16.bin", + "safety_checker/model.fp16.safetensors", + "vae/diffusion_pytorch_model.fp16.bin", + "vae/diffusion_pytorch_model.fp16.safetensors", + "text_encoder/pytorch_model.fp16.bin", + "unet/diffusion_pytorch_model.fp16.bin", + "unet/diffusion_pytorch_model.fp16.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames, folder_names={"vae", "unet"})) + self.assertTrue(is_safetensors_compatible(filenames, folder_names={"vae", "unet"}, variant="fp16")) + + def test_transformer_model_is_not_compatible_variant_extra_folder(self): + filenames = [ + "safety_checker/pytorch_model.fp16.bin", + "safety_checker/model.fp16.safetensors", + "vae/diffusion_pytorch_model.fp16.bin", + "vae/diffusion_pytorch_model.fp16.safetensors", + "text_encoder/pytorch_model.fp16.bin", + "unet/diffusion_pytorch_model.fp16.bin", + "unet/diffusion_pytorch_model.fp16.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames, folder_names={"text_encoder"})) + + def test_transformers_is_compatible_sharded(self): + filenames = [ + "text_encoder/pytorch_model.bin", + "text_encoder/model-00001-of-00002.safetensors", + "text_encoder/model-00002-of-00002.safetensors", + ] + self.assertTrue(is_safetensors_compatible(filenames)) + + def test_transformers_is_compatible_variant_sharded(self): + filenames = [ + "text_encoder/pytorch_model.bin", + "text_encoder/model.fp16-00001-of-00002.safetensors", + "text_encoder/model.fp16-00001-of-00002.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + self.assertTrue(is_safetensors_compatible(filenames, variant="fp16")) + + def test_diffusers_is_compatible_sharded(self): + filenames = [ + "unet/diffusion_pytorch_model.bin", + "unet/diffusion_pytorch_model-00001-of-00002.safetensors", + "unet/diffusion_pytorch_model-00002-of-00002.safetensors", + ] + self.assertTrue(is_safetensors_compatible(filenames)) + + def test_diffusers_is_compatible_variant_sharded(self): + filenames = [ + "unet/diffusion_pytorch_model.bin", + "unet/diffusion_pytorch_model.fp16-00001-of-00002.safetensors", + "unet/diffusion_pytorch_model.fp16-00001-of-00002.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + self.assertTrue(is_safetensors_compatible(filenames, variant="fp16")) + + def test_diffusers_is_compatible_only_variants(self): + filenames = [ + "unet/diffusion_pytorch_model.fp16.safetensors", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + self.assertTrue(is_safetensors_compatible(filenames, variant="fp16")) + + def test_diffusers_is_compatible_no_components(self): + filenames = [ + "diffusion_pytorch_model.bin", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + + def test_diffusers_is_compatible_no_components_only_variants(self): + filenames = [ + "diffusion_pytorch_model.fp16.bin", + ] + self.assertFalse(is_safetensors_compatible(filenames)) + + def test_is_compatible_mixed_variants(self): + filenames = [ + "unet/diffusion_pytorch_model.fp16.safetensors", + "vae/diffusion_pytorch_model.safetensors", + ] + self.assertTrue(is_safetensors_compatible(filenames, variant="fp16")) + + def test_is_compatible_variant_and_non_safetensors(self): + filenames = [ + "unet/diffusion_pytorch_model.fp16.safetensors", + "vae/diffusion_pytorch_model.bin", + ] + self.assertFalse(is_safetensors_compatible(filenames, variant="fp16")) + + +class VariantCompatibleSiblingsTest(unittest.TestCase): + def test_only_non_variants_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + f"vae/diffusion_pytorch_model.{variant}.safetensors", + "vae/diffusion_pytorch_model.safetensors", + f"text_encoder/model.{variant}.safetensors", + "text_encoder/model.safetensors", + f"unet/diffusion_pytorch_model.{variant}.safetensors", + "unet/diffusion_pytorch_model.safetensors", + ] + + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=None, ignore_patterns=ignore_patterns + ) + assert all(variant not in f for f in model_filenames) + + def test_only_variants_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + f"vae/diffusion_pytorch_model.{variant}.safetensors", + "vae/diffusion_pytorch_model.safetensors", + f"text_encoder/model.{variant}.safetensors", + "text_encoder/model.safetensors", + f"unet/diffusion_pytorch_model.{variant}.safetensors", + "unet/diffusion_pytorch_model.safetensors", + ] + + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f for f in model_filenames) + + def test_mixed_variants_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + non_variant_file = "text_encoder/model.safetensors" + filenames = [ + f"vae/diffusion_pytorch_model.{variant}.safetensors", + "vae/diffusion_pytorch_model.safetensors", + "text_encoder/model.safetensors", + f"unet/diffusion_pytorch_model.{variant}.safetensors", + "unet/diffusion_pytorch_model.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f if f != non_variant_file else variant not in f for f in model_filenames) + + def test_non_variants_in_main_dir_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + f"diffusion_pytorch_model.{variant}.safetensors", + "diffusion_pytorch_model.safetensors", + "model.safetensors", + f"model.{variant}.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=None, ignore_patterns=ignore_patterns + ) + assert all(variant not in f for f in model_filenames) + + def test_variants_in_main_dir_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + f"diffusion_pytorch_model.{variant}.safetensors", + "diffusion_pytorch_model.safetensors", + "model.safetensors", + f"model.{variant}.safetensors", + f"diffusion_pytorch_model.{variant}.safetensors", + "diffusion_pytorch_model.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f for f in model_filenames) + + def test_mixed_variants_in_main_dir_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + non_variant_file = "model.safetensors" + filenames = [ + f"diffusion_pytorch_model.{variant}.safetensors", + "diffusion_pytorch_model.safetensors", + "model.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f if f != non_variant_file else variant not in f for f in model_filenames) + + def test_sharded_variants_in_main_dir_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + "diffusion_pytorch_model.safetensors.index.json", + "diffusion_pytorch_model-00001-of-00003.safetensors", + "diffusion_pytorch_model-00002-of-00003.safetensors", + "diffusion_pytorch_model-00003-of-00003.safetensors", + f"diffusion_pytorch_model.{variant}-00001-of-00002.safetensors", + f"diffusion_pytorch_model.{variant}-00002-of-00002.safetensors", + f"diffusion_pytorch_model.safetensors.index.{variant}.json", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f for f in model_filenames) + + def test_mixed_sharded_and_variant_in_main_dir_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + "diffusion_pytorch_model.safetensors.index.json", + "diffusion_pytorch_model-00001-of-00003.safetensors", + "diffusion_pytorch_model-00002-of-00003.safetensors", + "diffusion_pytorch_model-00003-of-00003.safetensors", + f"diffusion_pytorch_model.{variant}.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f for f in model_filenames) + + def test_mixed_sharded_non_variants_in_main_dir_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + f"diffusion_pytorch_model.safetensors.index.{variant}.json", + "diffusion_pytorch_model.safetensors.index.json", + "diffusion_pytorch_model-00001-of-00003.safetensors", + "diffusion_pytorch_model-00002-of-00003.safetensors", + "diffusion_pytorch_model-00003-of-00003.safetensors", + f"diffusion_pytorch_model.{variant}-00001-of-00002.safetensors", + f"diffusion_pytorch_model.{variant}-00002-of-00002.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=None, ignore_patterns=ignore_patterns + ) + assert all(variant not in f for f in model_filenames) + + def test_sharded_non_variants_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + f"unet/diffusion_pytorch_model.safetensors.index.{variant}.json", + "unet/diffusion_pytorch_model.safetensors.index.json", + "unet/diffusion_pytorch_model-00001-of-00003.safetensors", + "unet/diffusion_pytorch_model-00002-of-00003.safetensors", + "unet/diffusion_pytorch_model-00003-of-00003.safetensors", + f"unet/diffusion_pytorch_model.{variant}-00001-of-00002.safetensors", + f"unet/diffusion_pytorch_model.{variant}-00002-of-00002.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=None, ignore_patterns=ignore_patterns + ) + assert all(variant not in f for f in model_filenames) + + def test_sharded_variants_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + f"unet/diffusion_pytorch_model.safetensors.index.{variant}.json", + "unet/diffusion_pytorch_model.safetensors.index.json", + "unet/diffusion_pytorch_model-00001-of-00003.safetensors", + "unet/diffusion_pytorch_model-00002-of-00003.safetensors", + "unet/diffusion_pytorch_model-00003-of-00003.safetensors", + f"unet/diffusion_pytorch_model.{variant}-00001-of-00002.safetensors", + f"unet/diffusion_pytorch_model.{variant}-00002-of-00002.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f for f in model_filenames) + assert model_filenames == variant_filenames + + def test_single_variant_with_sharded_non_variant_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + "unet/diffusion_pytorch_model.safetensors.index.json", + "unet/diffusion_pytorch_model-00001-of-00003.safetensors", + "unet/diffusion_pytorch_model-00002-of-00003.safetensors", + "unet/diffusion_pytorch_model-00003-of-00003.safetensors", + f"unet/diffusion_pytorch_model.{variant}.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f for f in model_filenames) + + def test_mixed_single_variant_with_sharded_non_variant_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + allowed_non_variant = "unet" + filenames = [ + "vae/diffusion_pytorch_model.safetensors.index.json", + "vae/diffusion_pytorch_model-00001-of-00003.safetensors", + "vae/diffusion_pytorch_model-00002-of-00003.safetensors", + "vae/diffusion_pytorch_model-00003-of-00003.safetensors", + f"vae/diffusion_pytorch_model.{variant}.safetensors", + "unet/diffusion_pytorch_model.safetensors.index.json", + "unet/diffusion_pytorch_model-00001-of-00003.safetensors", + "unet/diffusion_pytorch_model-00002-of-00003.safetensors", + "unet/diffusion_pytorch_model-00003-of-00003.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f if allowed_non_variant not in f else variant not in f for f in model_filenames) + + def test_sharded_mixed_variants_downloaded(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + allowed_non_variant = "unet" + filenames = [ + f"vae/diffusion_pytorch_model.safetensors.index.{variant}.json", + "vae/diffusion_pytorch_model.safetensors.index.json", + "unet/diffusion_pytorch_model.safetensors.index.json", + "unet/diffusion_pytorch_model-00001-of-00003.safetensors", + "unet/diffusion_pytorch_model-00002-of-00003.safetensors", + "unet/diffusion_pytorch_model-00003-of-00003.safetensors", + f"vae/diffusion_pytorch_model.{variant}-00001-of-00002.safetensors", + f"vae/diffusion_pytorch_model.{variant}-00002-of-00002.safetensors", + "vae/diffusion_pytorch_model-00001-of-00003.safetensors", + "vae/diffusion_pytorch_model-00002-of-00003.safetensors", + "vae/diffusion_pytorch_model-00003-of-00003.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f if allowed_non_variant not in f else variant not in f for f in model_filenames) + + def test_downloading_when_no_variant_exists(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = ["model.safetensors", "diffusion_pytorch_model.safetensors"] + with self.assertRaisesRegex(ValueError, "but no such modeling files are available. "): + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + + def test_downloading_use_safetensors_false(self): + ignore_patterns = ["*.safetensors"] + filenames = [ + "text_encoder/model.bin", + "unet/diffusion_pytorch_model.bin", + "unet/diffusion_pytorch_model.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=None, ignore_patterns=ignore_patterns + ) + + assert all(".safetensors" not in f for f in model_filenames) + + def test_non_variant_in_main_dir_with_variant_in_subfolder(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + allowed_non_variant = "diffusion_pytorch_model.safetensors" + filenames = [ + f"unet/diffusion_pytorch_model.{variant}.safetensors", + "diffusion_pytorch_model.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f if allowed_non_variant not in f else variant not in f for f in model_filenames) + + def test_download_variants_when_component_has_no_safetensors_variant(self): + ignore_patterns = None + variant = "fp16" + filenames = [ + f"unet/diffusion_pytorch_model.{variant}.bin", + "vae/diffusion_pytorch_model.safetensors", + f"vae/diffusion_pytorch_model.{variant}.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert { + f"unet/diffusion_pytorch_model.{variant}.bin", + f"vae/diffusion_pytorch_model.{variant}.safetensors", + } == model_filenames + + def test_error_when_download_sharded_variants_when_component_has_no_safetensors_variant(self): + ignore_patterns = ["*.bin"] + variant = "fp16" + filenames = [ + f"vae/diffusion_pytorch_model.bin.index.{variant}.json", + "vae/diffusion_pytorch_model.safetensors.index.json", + f"vae/diffusion_pytorch_model.{variant}-00002-of-00002.bin", + "vae/diffusion_pytorch_model-00001-of-00003.safetensors", + "vae/diffusion_pytorch_model-00002-of-00003.safetensors", + "vae/diffusion_pytorch_model-00003-of-00003.safetensors", + "unet/diffusion_pytorch_model.safetensors.index.json", + "unet/diffusion_pytorch_model-00001-of-00003.safetensors", + "unet/diffusion_pytorch_model-00002-of-00003.safetensors", + "unet/diffusion_pytorch_model-00003-of-00003.safetensors", + f"vae/diffusion_pytorch_model.{variant}-00001-of-00002.bin", + ] + with self.assertRaisesRegex(ValueError, "but no such modeling files are available. "): + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + + def test_download_sharded_variants_when_component_has_no_safetensors_variant_and_safetensors_false(self): + ignore_patterns = ["*.safetensors"] + allowed_non_variant = "unet" + variant = "fp16" + filenames = [ + f"vae/diffusion_pytorch_model.bin.index.{variant}.json", + "vae/diffusion_pytorch_model.safetensors.index.json", + f"vae/diffusion_pytorch_model.{variant}-00002-of-00002.bin", + "vae/diffusion_pytorch_model-00001-of-00003.safetensors", + "vae/diffusion_pytorch_model-00002-of-00003.safetensors", + "vae/diffusion_pytorch_model-00003-of-00003.safetensors", + "unet/diffusion_pytorch_model.safetensors.index.json", + "unet/diffusion_pytorch_model-00001-of-00003.safetensors", + "unet/diffusion_pytorch_model-00002-of-00003.safetensors", + "unet/diffusion_pytorch_model-00003-of-00003.safetensors", + f"vae/diffusion_pytorch_model.{variant}-00001-of-00002.bin", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f if allowed_non_variant not in f else variant not in f for f in model_filenames) + + def test_download_sharded_legacy_variants(self): + ignore_patterns = None + variant = "fp16" + filenames = [ + f"vae/transformer/diffusion_pytorch_model.safetensors.{variant}.index.json", + "vae/diffusion_pytorch_model.safetensors.index.json", + f"vae/diffusion_pytorch_model-00002-of-00002.{variant}.safetensors", + "vae/diffusion_pytorch_model-00001-of-00003.safetensors", + "vae/diffusion_pytorch_model-00002-of-00003.safetensors", + "vae/diffusion_pytorch_model-00003-of-00003.safetensors", + f"vae/diffusion_pytorch_model-00001-of-00002.{variant}.safetensors", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=variant, ignore_patterns=ignore_patterns + ) + assert all(variant in f for f in model_filenames) + + def test_download_onnx_models(self): + ignore_patterns = ["*.safetensors"] + filenames = [ + "vae/model.onnx", + "unet/model.onnx", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=None, ignore_patterns=ignore_patterns + ) + assert model_filenames == set(filenames) + + def test_download_flax_models(self): + ignore_patterns = ["*.safetensors", "*.bin"] + filenames = [ + "vae/diffusion_flax_model.msgpack", + "unet/diffusion_flax_model.msgpack", + ] + model_filenames, variant_filenames = variant_compatible_siblings( + filenames, variant=None, ignore_patterns=ignore_patterns + ) + assert model_filenames == set(filenames) + + +class ProgressBarTests(unittest.TestCase): + def get_dummy_components_image_generation(self): + cross_attention_dim = 8 + + torch.manual_seed(0) + unet = UNet2DConditionModel( + block_out_channels=(4, 8), + layers_per_block=1, + sample_size=32, + in_channels=4, + out_channels=4, + down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), + cross_attention_dim=cross_attention_dim, + norm_num_groups=2, + ) + scheduler = DDIMScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + ) + torch.manual_seed(0) + vae = AutoencoderKL( + block_out_channels=[4, 8], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + norm_num_groups=2, + ) + torch.manual_seed(0) + text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=cross_attention_dim, + intermediate_size=16, + layer_norm_eps=1e-05, + num_attention_heads=2, + num_hidden_layers=2, + pad_token_id=1, + vocab_size=1000, + ) + text_encoder = CLIPTextModel(text_encoder_config) + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + components = { + "unet": unet, + "scheduler": scheduler, + "vae": vae, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "safety_checker": None, + "feature_extractor": None, + "image_encoder": None, + } + return components + + def get_dummy_components_video_generation(self): + cross_attention_dim = 8 + block_out_channels = (8, 8) + + torch.manual_seed(0) + unet = UNet2DConditionModel( + block_out_channels=block_out_channels, + layers_per_block=2, + sample_size=8, + in_channels=4, + out_channels=4, + down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), + cross_attention_dim=cross_attention_dim, + norm_num_groups=2, + ) + scheduler = DDIMScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="linear", + clip_sample=False, + ) + torch.manual_seed(0) + vae = AutoencoderKL( + block_out_channels=block_out_channels, + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + norm_num_groups=2, + ) + torch.manual_seed(0) + text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=cross_attention_dim, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + ) + text_encoder = CLIPTextModel(text_encoder_config) + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + torch.manual_seed(0) + motion_adapter = MotionAdapter( + block_out_channels=block_out_channels, + motion_layers_per_block=2, + motion_norm_num_groups=2, + motion_num_attention_heads=4, + ) + + components = { + "unet": unet, + "scheduler": scheduler, + "vae": vae, + "motion_adapter": motion_adapter, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "feature_extractor": None, + "image_encoder": None, + } + return components + + def test_text_to_image(self): + components = self.get_dummy_components_image_generation() + pipe = StableDiffusionPipeline(**components) + pipe.to(torch_device) + + inputs = {"prompt": "a cute cat", "num_inference_steps": 2} + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + stderr = stderr.getvalue() + # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img, + # so we just match "5" in "#####| 1/5 [00:01<00:00]" + max_steps = re.search("/(.*?) ", stderr).group(1) + self.assertTrue(max_steps is not None and len(max_steps) > 0) + self.assertTrue( + f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step" + ) + + pipe.set_progress_bar_config(disable=True) + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled") + + def test_image_to_image(self): + components = self.get_dummy_components_image_generation() + pipe = StableDiffusionImg2ImgPipeline(**components) + pipe.to(torch_device) + + image = Image.new("RGB", (32, 32)) + inputs = {"prompt": "a cute cat", "num_inference_steps": 2, "strength": 0.5, "image": image} + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + stderr = stderr.getvalue() + # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img, + # so we just match "5" in "#####| 1/5 [00:01<00:00]" + max_steps = re.search("/(.*?) ", stderr).group(1) + self.assertTrue(max_steps is not None and len(max_steps) > 0) + self.assertTrue( + f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step" + ) + + pipe.set_progress_bar_config(disable=True) + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled") + + def test_inpainting(self): + components = self.get_dummy_components_image_generation() + pipe = StableDiffusionInpaintPipeline(**components) + pipe.to(torch_device) + + image = Image.new("RGB", (32, 32)) + mask = Image.new("RGB", (32, 32)) + inputs = { + "prompt": "a cute cat", + "num_inference_steps": 2, + "strength": 0.5, + "image": image, + "mask_image": mask, + } + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + stderr = stderr.getvalue() + # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img, + # so we just match "5" in "#####| 1/5 [00:01<00:00]" + max_steps = re.search("/(.*?) ", stderr).group(1) + self.assertTrue(max_steps is not None and len(max_steps) > 0) + self.assertTrue( + f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step" + ) + + pipe.set_progress_bar_config(disable=True) + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled") + + def test_text_to_video(self): + components = self.get_dummy_components_video_generation() + pipe = AnimateDiffPipeline(**components) + pipe.to(torch_device) + + inputs = {"prompt": "a cute cat", "num_inference_steps": 2, "num_frames": 2} + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + stderr = stderr.getvalue() + # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img, + # so we just match "5" in "#####| 1/5 [00:01<00:00]" + max_steps = re.search("/(.*?) ", stderr).group(1) + self.assertTrue(max_steps is not None and len(max_steps) > 0) + self.assertTrue( + f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step" + ) + + pipe.set_progress_bar_config(disable=True) + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled") + + def test_video_to_video(self): + components = self.get_dummy_components_video_generation() + pipe = AnimateDiffVideoToVideoPipeline(**components) + pipe.to(torch_device) + + num_frames = 2 + video = [Image.new("RGB", (32, 32))] * num_frames + inputs = {"prompt": "a cute cat", "num_inference_steps": 2, "video": video} + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + stderr = stderr.getvalue() + # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img, + # so we just match "5" in "#####| 1/5 [00:01<00:00]" + max_steps = re.search("/(.*?) ", stderr).group(1) + self.assertTrue(max_steps is not None and len(max_steps) > 0) + self.assertTrue( + f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step" + ) + + pipe.set_progress_bar_config(disable=True) + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled") + + +@require_torch_accelerator +class PipelineDeviceAndDtypeStabilityTests(unittest.TestCase): + expected_pipe_device = torch.device(f"{torch_device}:0") + expected_pipe_dtype = torch.float64 + + def get_dummy_components_image_generation(self): + cross_attention_dim = 8 + + torch.manual_seed(0) + unet = UNet2DConditionModel( + block_out_channels=(4, 8), + layers_per_block=1, + sample_size=32, + in_channels=4, + out_channels=4, + down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), + cross_attention_dim=cross_attention_dim, + norm_num_groups=2, + ) + scheduler = DDIMScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + ) + torch.manual_seed(0) + vae = AutoencoderKL( + block_out_channels=[4, 8], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + norm_num_groups=2, + ) + torch.manual_seed(0) + text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=cross_attention_dim, + intermediate_size=16, + layer_norm_eps=1e-05, + num_attention_heads=2, + num_hidden_layers=2, + pad_token_id=1, + vocab_size=1000, + ) + text_encoder = CLIPTextModel(text_encoder_config) + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + components = { + "unet": unet, + "scheduler": scheduler, + "vae": vae, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "safety_checker": None, + "feature_extractor": None, + "image_encoder": None, + } + return components + + def test_deterministic_device(self): + components = self.get_dummy_components_image_generation() + + pipe = StableDiffusionPipeline(**components) + pipe.to(device=torch_device, dtype=torch.float32) + + pipe.unet.to(device="cpu") + pipe.vae.to(device=torch_device) + pipe.text_encoder.to(device=f"{torch_device}:0") + + pipe_device = pipe.device + + self.assertEqual( + self.expected_pipe_device, + pipe_device, + f"Wrong expected device. Expected {self.expected_pipe_device}. Got {pipe_device}.", + ) + + def test_deterministic_dtype(self): + components = self.get_dummy_components_image_generation() + + pipe = StableDiffusionPipeline(**components) + pipe.to(device=torch_device, dtype=torch.float32) + + pipe.unet.to(dtype=torch.float16) + pipe.vae.to(dtype=torch.float32) + pipe.text_encoder.to(dtype=torch.float64) + + pipe_dtype = pipe.dtype + + self.assertEqual( + self.expected_pipe_dtype, + pipe_dtype, + f"Wrong expected dtype. Expected {self.expected_pipe_dtype}. Got {pipe_dtype}.", + ) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines.py new file mode 100644 index 0000000000000000000000000000000000000000..a17db3ff0c5a99c1c0f1d8346a96b02184eb5759 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines.py @@ -0,0 +1,2467 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import json +import os +import random +import re +import shutil +import sys +import tempfile +import traceback +import unittest +import unittest.mock as mock +import warnings + +import numpy as np +import PIL.Image +import pytest +import requests_mock +import safetensors.torch +import torch +import torch.nn as nn +from huggingface_hub import snapshot_download +from huggingface_hub.utils import HfHubHTTPError +from parameterized import parameterized +from PIL import Image +from transformers import CLIPImageProcessor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer + +from diffusers import ( + AutoencoderKL, + ConfigMixin, + DDIMPipeline, + DDIMScheduler, + DDPMPipeline, + DDPMScheduler, + DiffusionPipeline, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + ModelMixin, + PNDMScheduler, + StableDiffusionImg2ImgPipeline, + StableDiffusionInpaintPipelineLegacy, + StableDiffusionPipeline, + UNet2DConditionModel, + UNet2DModel, + UniPCMultistepScheduler, + logging, +) +from diffusers.pipelines.pipeline_utils import _get_pipeline_class +from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME +from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, is_transformers_version +from diffusers.utils.torch_utils import is_compiled_module + +from ..testing_utils import ( + CaptureLogger, + backend_empty_cache, + enable_full_determinism, + floats_tensor, + get_python_version, + get_tests_dir, + is_torch_compile, + load_numpy, + nightly, + require_compel, + require_flax, + require_hf_hub_version_greater, + require_onnxruntime, + require_peft_backend, + require_peft_version_greater, + require_torch_2, + require_torch_accelerator, + require_transformers_version_greater, + run_test_in_subprocess, + slow, + torch_device, +) + + +enable_full_determinism() + + +# Will be run via run_test_in_subprocess +def _test_from_save_pretrained_dynamo(in_queue, out_queue, timeout): + error = None + try: + # 1. Load models + model = UNet2DModel( + block_out_channels=(32, 64), + layers_per_block=2, + sample_size=32, + in_channels=3, + out_channels=3, + down_block_types=("DownBlock2D", "AttnDownBlock2D"), + up_block_types=("AttnUpBlock2D", "UpBlock2D"), + ) + model = torch.compile(model) + scheduler = DDPMScheduler(num_train_timesteps=10) + + ddpm = DDPMPipeline(model, scheduler) + + # previous diffusers versions stripped compilation off + # compiled modules + assert is_compiled_module(ddpm.unet) + + ddpm.to(torch_device) + ddpm.set_progress_bar_config(disable=None) + + with tempfile.TemporaryDirectory() as tmpdirname: + ddpm.save_pretrained(tmpdirname) + new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) + new_ddpm.to(torch_device) + + generator = torch.Generator(device=torch_device).manual_seed(0) + image = ddpm(generator=generator, num_inference_steps=5, output_type="np").images + + generator = torch.Generator(device=torch_device).manual_seed(0) + new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="np").images + + assert np.abs(image - new_image).max() < 1e-5, "Models don't give the same forward pass" + except Exception: + error = f"{traceback.format_exc()}" + + results = {"error": error} + out_queue.put(results, timeout=timeout) + out_queue.join() + + +class CustomEncoder(ModelMixin, ConfigMixin): + def __init__(self): + super().__init__() + self.linear = nn.Linear(3, 3) + + +class CustomPipeline(DiffusionPipeline): + def __init__(self, encoder: CustomEncoder, scheduler: DDIMScheduler): + super().__init__() + self.register_modules(encoder=encoder, scheduler=scheduler) + + +class DownloadTests(unittest.TestCase): + @unittest.skip("Flaky behaviour on CI. Re-enable after migrating to new runners") + def test_one_request_upon_cached(self): + # TODO: For some reason this test fails on MPS where no HEAD call is made. + if torch_device == "mps": + return + + with tempfile.TemporaryDirectory() as tmpdirname: + with requests_mock.mock(real_http=True) as m: + DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-pipe", cache_dir=tmpdirname) + + download_requests = [r.method for r in m.request_history] + assert download_requests.count("HEAD") == 15, "15 calls to files" + assert download_requests.count("GET") == 17, "15 calls to files + model_info + model_index.json" + assert len(download_requests) == 32, ( + "2 calls per file (15 files) + send_telemetry, model_info and model_index.json" + ) + + with requests_mock.mock(real_http=True) as m: + DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname + ) + + cache_requests = [r.method for r in m.request_history] + assert cache_requests.count("HEAD") == 1, "model_index.json is only HEAD" + assert cache_requests.count("GET") == 1, "model info is only GET" + assert len(cache_requests) == 2, ( + "We should call only `model_info` to check for _commit hash and `send_telemetry`" + ) + + def test_less_downloads_passed_object(self): + with tempfile.TemporaryDirectory() as tmpdirname: + cached_folder = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname + ) + + # make sure safety checker is not downloaded + assert "safety_checker" not in os.listdir(cached_folder) + + # make sure rest is downloaded + assert "unet" in os.listdir(cached_folder) + assert "tokenizer" in os.listdir(cached_folder) + assert "vae" in os.listdir(cached_folder) + assert "model_index.json" in os.listdir(cached_folder) + assert "scheduler" in os.listdir(cached_folder) + assert "feature_extractor" in os.listdir(cached_folder) + + @unittest.skip("Flaky behaviour on CI. Re-enable after migrating to new runners") + def test_less_downloads_passed_object_calls(self): + # TODO: For some reason this test fails on MPS where no HEAD call is made. + if torch_device == "mps": + return + + with tempfile.TemporaryDirectory() as tmpdirname: + with requests_mock.mock(real_http=True) as m: + DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname + ) + + download_requests = [r.method for r in m.request_history] + # 15 - 2 because no call to config or model file for `safety_checker` + assert download_requests.count("HEAD") == 13, "13 calls to files" + # 17 - 2 because no call to config or model file for `safety_checker` + assert download_requests.count("GET") == 15, "13 calls to files + model_info + model_index.json" + assert len(download_requests) == 28, ( + "2 calls per file (13 files) + send_telemetry, model_info and model_index.json" + ) + + with requests_mock.mock(real_http=True) as m: + DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname + ) + + cache_requests = [r.method for r in m.request_history] + assert cache_requests.count("HEAD") == 1, "model_index.json is only HEAD" + assert cache_requests.count("GET") == 1, "model info is only GET" + assert len(cache_requests) == 2, ( + "We should call only `model_info` to check for _commit hash and `send_telemetry`" + ) + + def test_download_only_pytorch(self): + with tempfile.TemporaryDirectory() as tmpdirname: + # pipeline has Flax weights + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname + ) + + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # None of the downloaded files should be a flax file even if we have some here: + # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack + assert not any(f.endswith(".msgpack") for f in files) + # We need to never convert this tiny model to safetensors for this test to pass + assert not any(f.endswith(".safetensors") for f in files) + + def test_force_safetensors_error(self): + with tempfile.TemporaryDirectory() as tmpdirname: + # pipeline has Flax weights + with self.assertRaises(EnvironmentError): + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe-no-safetensors", + safety_checker=None, + cache_dir=tmpdirname, + use_safetensors=True, + ) + + def test_download_safetensors(self): + with tempfile.TemporaryDirectory() as tmpdirname: + # pipeline has Flax weights + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe-safetensors", + safety_checker=None, + cache_dir=tmpdirname, + ) + + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # None of the downloaded files should be a pytorch file even if we have some here: + # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack + assert not any(f.endswith(".bin") for f in files) + + def test_download_safetensors_index(self): + for variant in ["fp16", None]: + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe-indexes", + cache_dir=tmpdirname, + use_safetensors=True, + variant=variant, + ) + + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # None of the downloaded files should be a safetensors file even if we have some here: + # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder + if variant is None: + assert not any("fp16" in f for f in files) + else: + model_files = [f for f in files if "safetensors" in f] + assert all("fp16" in f for f in model_files) + + assert len([f for f in files if ".safetensors" in f]) == 8 + assert not any(".bin" in f for f in files) + + def test_download_bin_index(self): + for variant in ["fp16", None]: + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe-indexes", + cache_dir=tmpdirname, + use_safetensors=False, + variant=variant, + ) + + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # None of the downloaded files should be a safetensors file even if we have some here: + # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder + if variant is None: + assert not any("fp16" in f for f in files) + else: + model_files = [f for f in files if "bin" in f] + assert all("fp16" in f for f in model_files) + + assert len([f for f in files if ".bin" in f]) == 8 + assert not any(".safetensors" in f for f in files) + + def test_download_no_openvino_by_default(self): + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-open-vino", + cache_dir=tmpdirname, + ) + + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # make sure that by default no openvino weights are downloaded + assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files) + assert not any("openvino_" in f for f in files) + + def test_download_no_onnx_by_default(self): + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-xl-pipe", + cache_dir=tmpdirname, + use_safetensors=False, + ) + + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # make sure that by default no onnx weights are downloaded for non-ONNX pipelines + assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files) + assert not any((f.endswith(".onnx") or f.endswith(".pb")) for f in files) + + @require_onnxruntime + def test_download_onnx_by_default_for_onnx_pipelines(self): + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline", + cache_dir=tmpdirname, + ) + + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # make sure that by default onnx weights are downloaded for ONNX pipelines + assert any((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files) + assert any((f.endswith(".onnx")) for f in files) + assert any((f.endswith(".pb")) for f in files) + + def test_download_no_safety_checker(self): + prompt = "hello" + pipe = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe = pipe.to(torch_device) + generator = torch.manual_seed(0) + out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="np").images + + pipe_2 = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") + pipe_2 = pipe_2.to(torch_device) + generator = torch.manual_seed(0) + out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="np").images + + assert np.max(np.abs(out - out_2)) < 1e-3 + + def test_load_no_safety_checker_explicit_locally(self): + prompt = "hello" + pipe = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe = pipe.to(torch_device) + generator = torch.manual_seed(0) + out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="np").images + + with tempfile.TemporaryDirectory() as tmpdirname: + pipe.save_pretrained(tmpdirname) + pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None) + pipe_2 = pipe_2.to(torch_device) + + generator = torch.manual_seed(0) + + out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="np").images + + assert np.max(np.abs(out - out_2)) < 1e-3 + + def test_load_no_safety_checker_default_locally(self): + prompt = "hello" + pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") + pipe = pipe.to(torch_device) + + generator = torch.manual_seed(0) + out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="np").images + + with tempfile.TemporaryDirectory() as tmpdirname: + pipe.save_pretrained(tmpdirname) + pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname) + pipe_2 = pipe_2.to(torch_device) + + generator = torch.manual_seed(0) + + out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="np").images + + assert np.max(np.abs(out - out_2)) < 1e-3 + + def test_cached_files_are_used_when_no_internet(self): + # A mock response for an HTTP head request to emulate server down + response_mock = mock.Mock() + response_mock.status_code = 500 + response_mock.headers = {} + response_mock.raise_for_status.side_effect = HfHubHTTPError("Server down", response=mock.Mock()) + response_mock.json.return_value = {} + + # Download this model to make sure it's in the cache. + orig_pipe = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, "parameters")} + + # Under the mock environment we get a 500 error when trying to reach the model. + with mock.patch("requests.request", return_value=response_mock): + # Download this model to make sure it's in the cache. + pipe = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + comps = {k: v for k, v in pipe.components.items() if hasattr(v, "parameters")} + + for m1, m2 in zip(orig_comps.values(), comps.values()): + for p1, p2 in zip(m1.parameters(), m2.parameters()): + if p1.data.ne(p2.data).sum() > 0: + assert False, "Parameters not the same!" + + def test_local_files_only_are_used_when_no_internet(self): + # A mock response for an HTTP head request to emulate server down + response_mock = mock.Mock() + response_mock.status_code = 500 + response_mock.headers = {} + response_mock.raise_for_status.side_effect = HfHubHTTPError("Server down", response=mock.Mock()) + response_mock.json.return_value = {} + + # first check that with local files only the pipeline can only be used if cached + with self.assertRaises(FileNotFoundError): + with tempfile.TemporaryDirectory() as tmpdirname: + orig_pipe = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", local_files_only=True, cache_dir=tmpdirname + ) + + # now download + orig_pipe = DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-torch") + + # make sure it can be loaded with local_files_only + orig_pipe = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", local_files_only=True + ) + orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, "parameters")} + + # Under the mock environment we get a 500 error when trying to connect to the internet. + # Make sure it works local_files_only only works here! + with mock.patch("requests.request", return_value=response_mock): + # Download this model to make sure it's in the cache. + pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") + comps = {k: v for k, v in pipe.components.items() if hasattr(v, "parameters")} + + for m1, m2 in zip(orig_comps.values(), comps.values()): + for p1, p2 in zip(m1.parameters(), m2.parameters()): + if p1.data.ne(p2.data).sum() > 0: + assert False, "Parameters not the same!" + + def test_download_from_variant_folder(self): + for use_safetensors in [False, True]: + other_format = ".bin" if use_safetensors else ".safetensors" + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = StableDiffusionPipeline.download( + "hf-internal-testing/stable-diffusion-all-variants", + cache_dir=tmpdirname, + use_safetensors=use_safetensors, + ) + all_root_files = [t[-1] for t in os.walk(tmpdirname)] + files = [item for sublist in all_root_files for item in sublist] + + # None of the downloaded files should be a variant file even if we have some here: + # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet + assert len(files) == 15, f"We should only download 15 files, not {len(files)}" + assert not any(f.endswith(other_format) for f in files) + # no variants + assert not any(len(f.split(".")) == 3 for f in files) + + def test_download_variant_all(self): + for use_safetensors in [False, True]: + other_format = ".bin" if use_safetensors else ".safetensors" + this_format = ".safetensors" if use_safetensors else ".bin" + variant = "fp16" + + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = StableDiffusionPipeline.download( + "hf-internal-testing/stable-diffusion-all-variants", + cache_dir=tmpdirname, + variant=variant, + use_safetensors=use_safetensors, + ) + all_root_files = [t[-1] for t in os.walk(tmpdirname)] + files = [item for sublist in all_root_files for item in sublist] + + # None of the downloaded files should be a non-variant file even if we have some here: + # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet + assert len(files) == 15, f"We should only download 15 files, not {len(files)}" + # unet, vae, text_encoder, safety_checker + assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 4 + # all checkpoints should have variant ending + assert not any(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files) + assert not any(f.endswith(other_format) for f in files) + + def test_download_variant_partly(self): + for use_safetensors in [False, True]: + other_format = ".bin" if use_safetensors else ".safetensors" + this_format = ".safetensors" if use_safetensors else ".bin" + variant = "no_ema" + + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = StableDiffusionPipeline.download( + "hf-internal-testing/stable-diffusion-all-variants", + cache_dir=tmpdirname, + variant=variant, + use_safetensors=use_safetensors, + ) + all_root_files = [t[-1] for t in os.walk(tmpdirname)] + files = [item for sublist in all_root_files for item in sublist] + + unet_files = os.listdir(os.path.join(tmpdirname, "unet")) + + # Some of the downloaded files should be a non-variant file, check: + # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet + assert len(files) == 15, f"We should only download 15 files, not {len(files)}" + # only unet has "no_ema" variant + assert f"diffusion_pytorch_model.{variant}{this_format}" in unet_files + assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 1 + # vae, safety_checker and text_encoder should have no variant + assert sum(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files) == 3 + assert not any(f.endswith(other_format) for f in files) + + def test_download_variants_with_sharded_checkpoints(self): + # Here we test for downloading of "variant" files belonging to the `unet` and + # the `text_encoder`. Their checkpoints can be sharded. + for use_safetensors in [True, False]: + for variant in ["fp16", None]: + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = DiffusionPipeline.download( + "hf-internal-testing/tiny-stable-diffusion-pipe-variants-right-format", + safety_checker=None, + cache_dir=tmpdirname, + variant=variant, + use_safetensors=use_safetensors, + ) + + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # Check for `model_ext` and `variant`. + model_ext = ".safetensors" if use_safetensors else ".bin" + unexpected_ext = ".bin" if use_safetensors else ".safetensors" + model_files = [f for f in files if f.endswith(model_ext)] + assert not any(f.endswith(unexpected_ext) for f in files) + assert all(variant in f for f in model_files if f.endswith(model_ext) and variant is not None) + + @pytest.mark.xfail(condition=is_transformers_version(">", "4.56.2"), reason="Some import error", strict=False) + def test_download_legacy_variants_with_sharded_ckpts_raises_warning(self): + repo_id = "hf-internal-testing/tiny-stable-diffusion-pipe-variants-all-kinds" + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + deprecated_warning_msg = "Warning: The repository contains sharded checkpoints for variant" + + with CaptureLogger(logger) as cap_logger: + with tempfile.TemporaryDirectory() as tmpdirname: + local_repo_id = snapshot_download(repo_id, cache_dir=tmpdirname) + + _ = DiffusionPipeline.from_pretrained( + local_repo_id, + safety_checker=None, + variant="fp16", + use_safetensors=True, + ) + assert deprecated_warning_msg in str(cap_logger), "Deprecation warning not found in logs" + + def test_download_safetensors_only_variant_exists_for_model(self): + variant = None + use_safetensors = True + + # text encoder is missing no variant weights, so the following can't work + with tempfile.TemporaryDirectory() as tmpdirname: + with self.assertRaises(OSError) as error_context: + tmpdirname = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/stable-diffusion-broken-variants", + cache_dir=tmpdirname, + variant=variant, + use_safetensors=use_safetensors, + ) + assert "Could not find the necessary `safetensors` weights" in str(error_context.exception) + + # text encoder has fp16 variants so we can load it + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = StableDiffusionPipeline.download( + "hf-internal-testing/stable-diffusion-broken-variants", + use_safetensors=use_safetensors, + cache_dir=tmpdirname, + variant="fp16", + ) + all_root_files = [t[-1] for t in os.walk(tmpdirname)] + files = [item for sublist in all_root_files for item in sublist] + # None of the downloaded files should be a non-variant file even if we have some here: + # https://huggingface.co/hf-internal-testing/stable-diffusion-broken-variants/tree/main/unet + assert len(files) == 15, f"We should only download 15 files, not {len(files)}" + + @pytest.mark.xfail(condition=is_transformers_version(">", "4.56.2"), reason="Some import error", strict=False) + def test_download_bin_only_variant_exists_for_model(self): + variant = None + use_safetensors = False + + # text encoder is missing Non-variant weights, so the following can't work + with tempfile.TemporaryDirectory() as tmpdirname: + with self.assertRaises(OSError) as error_context: + tmpdirname = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/stable-diffusion-broken-variants", + cache_dir=tmpdirname, + variant=variant, + use_safetensors=use_safetensors, + ) + assert "Error no file name" in str(error_context.exception) + + # text encoder has fp16 variants so we can load it + with tempfile.TemporaryDirectory() as tmpdirname: + tmpdirname = StableDiffusionPipeline.download( + "hf-internal-testing/stable-diffusion-broken-variants", + use_safetensors=use_safetensors, + cache_dir=tmpdirname, + variant="fp16", + ) + all_root_files = [t[-1] for t in os.walk(tmpdirname)] + files = [item for sublist in all_root_files for item in sublist] + # None of the downloaded files should be a non-variant file even if we have some here: + # https://huggingface.co/hf-internal-testing/stable-diffusion-broken-variants/tree/main/unet + assert len(files) == 15, f"We should only download 15 files, not {len(files)}" + + def test_download_safetensors_variant_does_not_exist_for_model(self): + variant = "no_ema" + use_safetensors = True + + # text encoder is missing no_ema variant weights, so the following can't work + with tempfile.TemporaryDirectory() as tmpdirname: + with self.assertRaises(OSError) as error_context: + tmpdirname = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/stable-diffusion-broken-variants", + cache_dir=tmpdirname, + variant=variant, + use_safetensors=use_safetensors, + ) + + assert "Could not find the necessary `safetensors` weights" in str(error_context.exception) + + @pytest.mark.xfail(condition=is_transformers_version(">", "4.56.2"), reason="Some import error", strict=False) + def test_download_bin_variant_does_not_exist_for_model(self): + variant = "no_ema" + use_safetensors = False + + # text encoder is missing no_ema variant weights, so the following can't work + with tempfile.TemporaryDirectory() as tmpdirname: + with self.assertRaises(OSError) as error_context: + tmpdirname = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/stable-diffusion-broken-variants", + cache_dir=tmpdirname, + variant=variant, + use_safetensors=use_safetensors, + ) + assert "Error no file name" in str(error_context.exception) + + @pytest.mark.xfail(condition=is_transformers_version(">", "4.56.2"), reason="Some import error", strict=False) + def test_local_save_load_index(self): + prompt = "hello" + for variant in [None, "fp16"]: + for use_safe in [True, False]: + pipe = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-pipe-indexes", + variant=variant, + use_safetensors=use_safe, + safety_checker=None, + ) + pipe = pipe.to(torch_device) + generator = torch.manual_seed(0) + out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="np").images + + with tempfile.TemporaryDirectory() as tmpdirname: + pipe.save_pretrained(tmpdirname, variant=variant, safe_serialization=use_safe) + pipe_2 = StableDiffusionPipeline.from_pretrained( + tmpdirname, safe_serialization=use_safe, variant=variant + ) + pipe_2 = pipe_2.to(torch_device) + + generator = torch.manual_seed(0) + + out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="np").images + + assert np.max(np.abs(out - out_2)) < 1e-3 + + def test_text_inversion_download(self): + pipe = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe = pipe.to(torch_device) + + num_tokens = len(pipe.tokenizer) + + # single token load local + with tempfile.TemporaryDirectory() as tmpdirname: + ten = {"<*>": torch.ones((32,))} + torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) + + pipe.load_textual_inversion(tmpdirname) + + token = pipe.tokenizer.convert_tokens_to_ids("<*>") + assert token == num_tokens, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32 + assert pipe._maybe_convert_prompt("<*>", pipe.tokenizer) == "<*>" + + prompt = "hey <*>" + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + # single token load local with weight name + with tempfile.TemporaryDirectory() as tmpdirname: + ten = {"<**>": 2 * torch.ones((1, 32))} + torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) + + pipe.load_textual_inversion(tmpdirname, weight_name="learned_embeds.bin") + + token = pipe.tokenizer.convert_tokens_to_ids("<**>") + assert token == num_tokens + 1, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64 + assert pipe._maybe_convert_prompt("<**>", pipe.tokenizer) == "<**>" + + prompt = "hey <**>" + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + # multi token load + with tempfile.TemporaryDirectory() as tmpdirname: + ten = {"<***>": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])} + torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) + + pipe.load_textual_inversion(tmpdirname) + + token = pipe.tokenizer.convert_tokens_to_ids("<***>") + token_1 = pipe.tokenizer.convert_tokens_to_ids("<***>_1") + token_2 = pipe.tokenizer.convert_tokens_to_ids("<***>_2") + + assert token == num_tokens + 2, "Added token must be at spot `num_tokens`" + assert token_1 == num_tokens + 3, "Added token must be at spot `num_tokens`" + assert token_2 == num_tokens + 4, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96 + assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128 + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160 + assert pipe._maybe_convert_prompt("<***>", pipe.tokenizer) == "<***> <***>_1 <***>_2" + + prompt = "hey <***>" + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + # multi token load a1111 + with tempfile.TemporaryDirectory() as tmpdirname: + ten = { + "string_to_param": { + "*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))]) + }, + "name": "<****>", + } + torch.save(ten, os.path.join(tmpdirname, "a1111.bin")) + + pipe.load_textual_inversion(tmpdirname, weight_name="a1111.bin") + + token = pipe.tokenizer.convert_tokens_to_ids("<****>") + token_1 = pipe.tokenizer.convert_tokens_to_ids("<****>_1") + token_2 = pipe.tokenizer.convert_tokens_to_ids("<****>_2") + + assert token == num_tokens + 5, "Added token must be at spot `num_tokens`" + assert token_1 == num_tokens + 6, "Added token must be at spot `num_tokens`" + assert token_2 == num_tokens + 7, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96 + assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128 + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160 + assert pipe._maybe_convert_prompt("<****>", pipe.tokenizer) == "<****> <****>_1 <****>_2" + + prompt = "hey <****>" + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + # multi embedding load + with tempfile.TemporaryDirectory() as tmpdirname1: + with tempfile.TemporaryDirectory() as tmpdirname2: + ten = {"<*****>": torch.ones((32,))} + torch.save(ten, os.path.join(tmpdirname1, "learned_embeds.bin")) + + ten = {"<******>": 2 * torch.ones((1, 32))} + torch.save(ten, os.path.join(tmpdirname2, "learned_embeds.bin")) + + pipe.load_textual_inversion([tmpdirname1, tmpdirname2]) + + token = pipe.tokenizer.convert_tokens_to_ids("<*****>") + assert token == num_tokens + 8, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32 + assert pipe._maybe_convert_prompt("<*****>", pipe.tokenizer) == "<*****>" + + token = pipe.tokenizer.convert_tokens_to_ids("<******>") + assert token == num_tokens + 9, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64 + assert pipe._maybe_convert_prompt("<******>", pipe.tokenizer) == "<******>" + + prompt = "hey <*****> <******>" + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + # single token state dict load + ten = {"": torch.ones((32,))} + pipe.load_textual_inversion(ten) + + token = pipe.tokenizer.convert_tokens_to_ids("") + assert token == num_tokens + 10, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32 + assert pipe._maybe_convert_prompt("", pipe.tokenizer) == "" + + prompt = "hey " + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + # multi embedding state dict load + ten1 = {"": torch.ones((32,))} + ten2 = {"": 2 * torch.ones((1, 32))} + + pipe.load_textual_inversion([ten1, ten2]) + + token = pipe.tokenizer.convert_tokens_to_ids("") + assert token == num_tokens + 11, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32 + assert pipe._maybe_convert_prompt("", pipe.tokenizer) == "" + + token = pipe.tokenizer.convert_tokens_to_ids("") + assert token == num_tokens + 12, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64 + assert pipe._maybe_convert_prompt("", pipe.tokenizer) == "" + + prompt = "hey " + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + # auto1111 multi-token state dict load + ten = { + "string_to_param": { + "*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))]) + }, + "name": "", + } + + pipe.load_textual_inversion(ten) + + token = pipe.tokenizer.convert_tokens_to_ids("") + token_1 = pipe.tokenizer.convert_tokens_to_ids("_1") + token_2 = pipe.tokenizer.convert_tokens_to_ids("_2") + + assert token == num_tokens + 13, "Added token must be at spot `num_tokens`" + assert token_1 == num_tokens + 14, "Added token must be at spot `num_tokens`" + assert token_2 == num_tokens + 15, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96 + assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128 + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160 + assert pipe._maybe_convert_prompt("", pipe.tokenizer) == " _1 _2" + + prompt = "hey " + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + # multiple references to multi embedding + ten = {"": torch.ones(3, 32)} + pipe.load_textual_inversion(ten) + + assert ( + pipe._maybe_convert_prompt(" ", pipe.tokenizer) == " _1 _2 _1 _2" + ) + + prompt = "hey " + out = pipe(prompt, num_inference_steps=1, output_type="np").images + assert out.shape == (1, 128, 128, 3) + + def test_text_inversion_multi_tokens(self): + pipe1 = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe1 = pipe1.to(torch_device) + + token1, token2 = "<*>", "<**>" + ten1 = torch.ones((32,)) + ten2 = torch.ones((32,)) * 2 + + num_tokens = len(pipe1.tokenizer) + + pipe1.load_textual_inversion(ten1, token=token1) + pipe1.load_textual_inversion(ten2, token=token2) + emb1 = pipe1.text_encoder.get_input_embeddings().weight + + pipe2 = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe2 = pipe2.to(torch_device) + pipe2.load_textual_inversion([ten1, ten2], token=[token1, token2]) + emb2 = pipe2.text_encoder.get_input_embeddings().weight + + pipe3 = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe3 = pipe3.to(torch_device) + pipe3.load_textual_inversion(torch.stack([ten1, ten2], dim=0), token=[token1, token2]) + emb3 = pipe3.text_encoder.get_input_embeddings().weight + + assert len(pipe1.tokenizer) == len(pipe2.tokenizer) == len(pipe3.tokenizer) == num_tokens + 2 + assert ( + pipe1.tokenizer.convert_tokens_to_ids(token1) + == pipe2.tokenizer.convert_tokens_to_ids(token1) + == pipe3.tokenizer.convert_tokens_to_ids(token1) + == num_tokens + ) + assert ( + pipe1.tokenizer.convert_tokens_to_ids(token2) + == pipe2.tokenizer.convert_tokens_to_ids(token2) + == pipe3.tokenizer.convert_tokens_to_ids(token2) + == num_tokens + 1 + ) + assert emb1[num_tokens].sum().item() == emb2[num_tokens].sum().item() == emb3[num_tokens].sum().item() + assert ( + emb1[num_tokens + 1].sum().item() == emb2[num_tokens + 1].sum().item() == emb3[num_tokens + 1].sum().item() + ) + + def test_textual_inversion_unload(self): + pipe1 = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe1 = pipe1.to(torch_device) + orig_tokenizer_size = len(pipe1.tokenizer) + orig_emb_size = len(pipe1.text_encoder.get_input_embeddings().weight) + + token = "<*>" + ten = torch.ones((32,)) + pipe1.load_textual_inversion(ten, token=token) + pipe1.unload_textual_inversion() + pipe1.load_textual_inversion(ten, token=token) + pipe1.unload_textual_inversion() + + final_tokenizer_size = len(pipe1.tokenizer) + final_emb_size = len(pipe1.text_encoder.get_input_embeddings().weight) + # both should be restored to original size + assert final_tokenizer_size == orig_tokenizer_size + assert final_emb_size == orig_emb_size + + def test_download_ignore_files(self): + # Check https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files/blob/72f58636e5508a218c6b3f60550dc96445547817/model_index.json#L4 + with tempfile.TemporaryDirectory() as tmpdirname: + # pipeline has Flax weights + tmpdirname = DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files") + all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] + files = [item for sublist in all_root_files for item in sublist] + + # None of the downloaded files should be a pytorch file even if we have some here: + # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack + assert not any(f in ["vae/diffusion_pytorch_model.bin", "text_encoder/config.json"] for f in files) + assert len(files) == 14 + + def test_download_dduf_with_custom_pipeline_raises_error(self): + with self.assertRaises(NotImplementedError): + _ = DiffusionPipeline.download( + "DDUF/tiny-flux-dev-pipe-dduf", dduf_file="fluxpipeline.dduf", custom_pipeline="my_pipeline" + ) + + def test_download_dduf_with_connected_pipeline_raises_error(self): + with self.assertRaises(NotImplementedError): + _ = DiffusionPipeline.download( + "DDUF/tiny-flux-dev-pipe-dduf", dduf_file="fluxpipeline.dduf", load_connected_pipeline=True + ) + + def test_get_pipeline_class_from_flax(self): + flax_config = {"_class_name": "FlaxStableDiffusionPipeline"} + config = {"_class_name": "StableDiffusionPipeline"} + + # when loading a PyTorch Pipeline from a FlaxPipeline `model_index.json`, e.g.: https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-lms-pipe/blob/7a9063578b325779f0f1967874a6771caa973cad/model_index.json#L2 + # we need to make sure that we don't load the Flax Pipeline class, but instead the PyTorch pipeline class + assert _get_pipeline_class(DiffusionPipeline, flax_config) == _get_pipeline_class(DiffusionPipeline, config) + + +class CustomPipelineTests(unittest.TestCase): + def test_load_custom_pipeline(self): + pipeline = DiffusionPipeline.from_pretrained( + "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline" + ) + pipeline = pipeline.to(torch_device) + # NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub + # under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24 + assert pipeline.__class__.__name__ == "CustomPipeline" + + def test_load_custom_github(self): + pipeline = DiffusionPipeline.from_pretrained( + "google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="main" + ) + + # make sure that on "main" pipeline gives only ones because of: https://github.com/huggingface/diffusers/pull/1690 + with torch.no_grad(): + output = pipeline() + + assert output.numel() == output.sum() + + # hack since Python doesn't like overwriting modules: https://stackoverflow.com/questions/3105801/unload-a-module-in-python + # Could in the future work with hashes instead. + del sys.modules["diffusers_modules.git.one_step_unet"] + + pipeline = DiffusionPipeline.from_pretrained( + "google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="0.10.2" + ) + with torch.no_grad(): + output = pipeline() + + assert output.numel() != output.sum() + + assert pipeline.__class__.__name__ == "UnetSchedulerOneForwardPipeline" + + def test_run_custom_pipeline(self): + pipeline = DiffusionPipeline.from_pretrained( + "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline" + ) + pipeline = pipeline.to(torch_device) + images, output_str = pipeline(num_inference_steps=2, output_type="np") + + assert images[0].shape == (1, 32, 32, 3) + + # compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102 + assert output_str == "This is a test" + + def test_remote_components(self): + # make sure that trust remote code has to be passed + with self.assertRaises(ValueError): + pipeline = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sdxl-custom-components") + + # Check that only loading custom components "my_unet", "my_scheduler" works + pipeline = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-sdxl-custom-components", trust_remote_code=True + ) + + assert pipeline.config.unet == ("diffusers_modules.local.my_unet_model", "MyUNetModel") + assert pipeline.config.scheduler == ("diffusers_modules.local.my_scheduler", "MyScheduler") + assert pipeline.__class__.__name__ == "StableDiffusionXLPipeline" + + pipeline = pipeline.to(torch_device) + images = pipeline("test", num_inference_steps=2, output_type="np")[0] + + assert images.shape == (1, 64, 64, 3) + + # Check that only loading custom components "my_unet", "my_scheduler" and explicit custom pipeline works + pipeline = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-sdxl-custom-components", custom_pipeline="my_pipeline", trust_remote_code=True + ) + + assert pipeline.config.unet == ("diffusers_modules.local.my_unet_model", "MyUNetModel") + assert pipeline.config.scheduler == ("diffusers_modules.local.my_scheduler", "MyScheduler") + assert pipeline.__class__.__name__ == "MyPipeline" + + pipeline = pipeline.to(torch_device) + images = pipeline("test", num_inference_steps=2, output_type="np")[0] + + assert images.shape == (1, 64, 64, 3) + + def test_remote_auto_custom_pipe(self): + # make sure that trust remote code has to be passed + with self.assertRaises(ValueError): + pipeline = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sdxl-custom-all") + + # Check that only loading custom components "my_unet", "my_scheduler" and auto custom pipeline works + pipeline = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-sdxl-custom-all", trust_remote_code=True + ) + + assert pipeline.config.unet == ("diffusers_modules.local.my_unet_model", "MyUNetModel") + assert pipeline.config.scheduler == ("diffusers_modules.local.my_scheduler", "MyScheduler") + assert pipeline.__class__.__name__ == "MyPipeline" + + pipeline = pipeline.to(torch_device) + images = pipeline("test", num_inference_steps=2, output_type="np")[0] + + assert images.shape == (1, 64, 64, 3) + + def test_remote_custom_pipe_with_dot_in_name(self): + # make sure that trust remote code has to be passed + with self.assertRaises(ValueError): + pipeline = DiffusionPipeline.from_pretrained("akasharidas/ddpm-cifar10-32-dot.in.name") + + pipeline = DiffusionPipeline.from_pretrained("akasharidas/ddpm-cifar10-32-dot.in.name", trust_remote_code=True) + + assert pipeline.__class__.__name__ == "CustomPipeline" + + pipeline = pipeline.to(torch_device) + images, output_str = pipeline(num_inference_steps=2, output_type="np") + + assert images[0].shape == (1, 32, 32, 3) + assert output_str == "This is a test" + + def test_local_custom_pipeline_repo(self): + local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline") + pipeline = DiffusionPipeline.from_pretrained( + "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path + ) + pipeline = pipeline.to(torch_device) + images, output_str = pipeline(num_inference_steps=2, output_type="np") + + assert pipeline.__class__.__name__ == "CustomLocalPipeline" + assert images[0].shape == (1, 32, 32, 3) + # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102 + assert output_str == "This is a local test" + + def test_local_custom_pipeline_file(self): + local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline") + local_custom_pipeline_path = os.path.join(local_custom_pipeline_path, "what_ever.py") + pipeline = DiffusionPipeline.from_pretrained( + "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path + ) + pipeline = pipeline.to(torch_device) + images, output_str = pipeline(num_inference_steps=2, output_type="np") + + assert pipeline.__class__.__name__ == "CustomLocalPipeline" + assert images[0].shape == (1, 32, 32, 3) + # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102 + assert output_str == "This is a local test" + + def test_custom_model_and_pipeline(self): + pipe = CustomPipeline( + encoder=CustomEncoder(), + scheduler=DDIMScheduler(), + ) + + with tempfile.TemporaryDirectory() as tmpdirname: + pipe.save_pretrained(tmpdirname, safe_serialization=False) + + pipe_new = CustomPipeline.from_pretrained(tmpdirname) + pipe_new.save_pretrained(tmpdirname) + + conf_1 = dict(pipe.config) + conf_2 = dict(pipe_new.config) + + del conf_2["_name_or_path"] + + assert conf_1 == conf_2 + + @slow + @require_torch_accelerator + def test_download_from_git(self): + # Because adaptive_avg_pool2d_backward_cuda + # does not have a deterministic implementation. + clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K" + + feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id) + clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16) + + pipeline = DiffusionPipeline.from_pretrained( + "CompVis/stable-diffusion-v1-4", + custom_pipeline="clip_guided_stable_diffusion", + clip_model=clip_model, + feature_extractor=feature_extractor, + torch_dtype=torch.float16, + ) + pipeline.enable_attention_slicing() + pipeline = pipeline.to(torch_device) + + # NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under: + # https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py + assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion" + + image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0] + assert image.shape == (512, 512, 3) + + def test_save_pipeline_change_config(self): + pipe = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + + with tempfile.TemporaryDirectory() as tmpdirname: + pipe.save_pretrained(tmpdirname) + pipe = DiffusionPipeline.from_pretrained(tmpdirname) + + assert pipe.scheduler.__class__.__name__ == "PNDMScheduler" + + # let's make sure that changing the scheduler is correctly reflected + with tempfile.TemporaryDirectory() as tmpdirname: + pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) + pipe.save_pretrained(tmpdirname) + pipe = DiffusionPipeline.from_pretrained(tmpdirname) + + assert pipe.scheduler.__class__.__name__ == "DPMSolverMultistepScheduler" + + +class PipelineFastTests(unittest.TestCase): + def setUp(self): + # clean up the VRAM before each test + super().setUp() + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + # clean up the VRAM after each test + super().tearDown() + gc.collect() + backend_empty_cache(torch_device) + + def dummy_image(self): + batch_size = 1 + num_channels = 3 + sizes = (32, 32) + + image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) + return image + + def dummy_uncond_unet(self, sample_size=32): + torch.manual_seed(0) + model = UNet2DModel( + block_out_channels=(32, 64), + layers_per_block=2, + sample_size=sample_size, + in_channels=3, + out_channels=3, + down_block_types=("DownBlock2D", "AttnDownBlock2D"), + up_block_types=("AttnUpBlock2D", "UpBlock2D"), + ) + return model + + def dummy_cond_unet(self, sample_size=32): + torch.manual_seed(0) + model = UNet2DConditionModel( + block_out_channels=(32, 64), + layers_per_block=2, + sample_size=sample_size, + in_channels=4, + out_channels=4, + down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), + cross_attention_dim=32, + ) + return model + + @property + def dummy_vae(self): + torch.manual_seed(0) + model = AutoencoderKL( + block_out_channels=[32, 64], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + ) + return model + + @property + def dummy_text_encoder(self): + torch.manual_seed(0) + config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + ) + return CLIPTextModel(config) + + @property + def dummy_extractor(self): + def extract(*args, **kwargs): + class Out: + def __init__(self): + self.pixel_values = torch.ones([0]) + + def to(self, device): + self.pixel_values.to(device) + return self + + return Out() + + return extract + + @parameterized.expand( + [ + [DDIMScheduler, DDIMPipeline, 32], + [DDPMScheduler, DDPMPipeline, 32], + [DDIMScheduler, DDIMPipeline, (32, 64)], + [DDPMScheduler, DDPMPipeline, (64, 32)], + ] + ) + def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32): + unet = self.dummy_uncond_unet(sample_size) + scheduler = scheduler_fn() + pipeline = pipeline_fn(unet, scheduler).to(torch_device) + + generator = torch.manual_seed(0) + out_image = pipeline( + generator=generator, + num_inference_steps=2, + output_type="np", + ).images + sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size + assert out_image.shape == (1, *sample_size, 3) + + def test_stable_diffusion_components(self): + """Test that components property works correctly""" + unet = self.dummy_cond_unet() + scheduler = PNDMScheduler(skip_prk_steps=True) + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + image = self.dummy_image().cpu().permute(0, 2, 3, 1)[0] + init_image = Image.fromarray(np.uint8(image)).convert("RGB") + mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32)) + + # make sure here that pndm scheduler skips prk + inpaint = StableDiffusionInpaintPipelineLegacy( + unet=unet, + scheduler=scheduler, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ).to(torch_device) + img2img = StableDiffusionImg2ImgPipeline(**inpaint.components, image_encoder=None).to(torch_device) + text2img = StableDiffusionPipeline(**inpaint.components, image_encoder=None).to(torch_device) + + prompt = "A painting of a squirrel eating a burger" + + generator = torch.manual_seed(0) + image_inpaint = inpaint( + [prompt], + generator=generator, + num_inference_steps=2, + output_type="np", + image=init_image, + mask_image=mask_image, + ).images + image_img2img = img2img( + [prompt], + generator=generator, + num_inference_steps=2, + output_type="np", + image=init_image, + ).images + image_text2img = text2img( + [prompt], + generator=generator, + num_inference_steps=2, + output_type="np", + ).images + + assert image_inpaint.shape == (1, 32, 32, 3) + assert image_img2img.shape == (1, 32, 32, 3) + assert image_text2img.shape == (1, 64, 64, 3) + + @require_torch_accelerator + def test_pipe_false_offload_warn(self): + unet = self.dummy_cond_unet() + scheduler = PNDMScheduler(skip_prk_steps=True) + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + sd = StableDiffusionPipeline( + unet=unet, + scheduler=scheduler, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ) + + sd.enable_model_cpu_offload(device=torch_device) + + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + with CaptureLogger(logger) as cap_logger: + sd.to(torch_device) + + assert "It is strongly recommended against doing so" in str(cap_logger) + + sd = StableDiffusionPipeline( + unet=unet, + scheduler=scheduler, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ) + + def test_set_scheduler(self): + unet = self.dummy_cond_unet() + scheduler = PNDMScheduler(skip_prk_steps=True) + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + sd = StableDiffusionPipeline( + unet=unet, + scheduler=scheduler, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ) + + sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config) + assert isinstance(sd.scheduler, DDIMScheduler) + sd.scheduler = DDPMScheduler.from_config(sd.scheduler.config) + assert isinstance(sd.scheduler, DDPMScheduler) + sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config) + assert isinstance(sd.scheduler, PNDMScheduler) + sd.scheduler = LMSDiscreteScheduler.from_config(sd.scheduler.config) + assert isinstance(sd.scheduler, LMSDiscreteScheduler) + sd.scheduler = EulerDiscreteScheduler.from_config(sd.scheduler.config) + assert isinstance(sd.scheduler, EulerDiscreteScheduler) + sd.scheduler = EulerAncestralDiscreteScheduler.from_config(sd.scheduler.config) + assert isinstance(sd.scheduler, EulerAncestralDiscreteScheduler) + sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config) + assert isinstance(sd.scheduler, DPMSolverMultistepScheduler) + + def test_set_component_to_none(self): + unet = self.dummy_cond_unet() + scheduler = PNDMScheduler(skip_prk_steps=True) + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + pipeline = StableDiffusionPipeline( + unet=unet, + scheduler=scheduler, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ) + + generator = torch.Generator(device="cpu").manual_seed(0) + + prompt = "This is a flower" + + out_image = pipeline( + prompt=prompt, + generator=generator, + num_inference_steps=1, + output_type="np", + ).images + + pipeline.feature_extractor = None + generator = torch.Generator(device="cpu").manual_seed(0) + out_image_2 = pipeline( + prompt=prompt, + generator=generator, + num_inference_steps=1, + output_type="np", + ).images + + assert out_image.shape == (1, 64, 64, 3) + assert np.abs(out_image - out_image_2).max() < 1e-3 + + def test_optional_components_is_none(self): + unet = self.dummy_cond_unet() + scheduler = PNDMScheduler(skip_prk_steps=True) + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + items = { + "feature_extractor": self.dummy_extractor, + "unet": unet, + "scheduler": scheduler, + "vae": vae, + "text_encoder": bert, + "tokenizer": tokenizer, + "safety_checker": None, + # we don't add an image encoder + } + + pipeline = StableDiffusionPipeline(**items) + + assert sorted(pipeline.components.keys()) == sorted(["image_encoder"] + list(items.keys())) + assert pipeline.image_encoder is None + + def test_set_scheduler_consistency(self): + unet = self.dummy_cond_unet() + pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler") + ddim = DDIMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler") + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + sd = StableDiffusionPipeline( + unet=unet, + scheduler=pndm, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ) + + pndm_config = sd.scheduler.config + sd.scheduler = DDPMScheduler.from_config(pndm_config) + sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config) + pndm_config_2 = sd.scheduler.config + pndm_config_2 = {k: v for k, v in pndm_config_2.items() if k in pndm_config} + + assert dict(pndm_config) == dict(pndm_config_2) + + sd = StableDiffusionPipeline( + unet=unet, + scheduler=ddim, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ) + + ddim_config = sd.scheduler.config + sd.scheduler = LMSDiscreteScheduler.from_config(ddim_config) + sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config) + ddim_config_2 = sd.scheduler.config + ddim_config_2 = {k: v for k, v in ddim_config_2.items() if k in ddim_config} + + assert dict(ddim_config) == dict(ddim_config_2) + + def test_save_safe_serialization(self): + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") + with tempfile.TemporaryDirectory() as tmpdirname: + pipeline.save_pretrained(tmpdirname, safe_serialization=True) + + # Validate that the VAE safetensor exists and are of the correct format + vae_path = os.path.join(tmpdirname, "vae", "diffusion_pytorch_model.safetensors") + assert os.path.exists(vae_path), f"Could not find {vae_path}" + _ = safetensors.torch.load_file(vae_path) + + # Validate that the UNet safetensor exists and are of the correct format + unet_path = os.path.join(tmpdirname, "unet", "diffusion_pytorch_model.safetensors") + assert os.path.exists(unet_path), f"Could not find {unet_path}" + _ = safetensors.torch.load_file(unet_path) + + # Validate that the text encoder safetensor exists and are of the correct format + text_encoder_path = os.path.join(tmpdirname, "text_encoder", "model.safetensors") + assert os.path.exists(text_encoder_path), f"Could not find {text_encoder_path}" + _ = safetensors.torch.load_file(text_encoder_path) + + pipeline = StableDiffusionPipeline.from_pretrained(tmpdirname) + assert pipeline.unet is not None + assert pipeline.vae is not None + assert pipeline.text_encoder is not None + assert pipeline.scheduler is not None + assert pipeline.feature_extractor is not None + + @pytest.mark.xfail(condition=is_transformers_version(">", "4.56.2"), reason="Some import error", strict=False) + def test_no_pytorch_download_when_doing_safetensors(self): + # by default we don't download + with tempfile.TemporaryDirectory() as tmpdirname: + _ = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname + ) + + path = os.path.join( + tmpdirname, + "models--hf-internal-testing--diffusers-stable-diffusion-tiny-all", + "snapshots", + "07838d72e12f9bcec1375b0482b80c1d399be843", + "unet", + ) + # safetensors exists + assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors")) + # pytorch does not + assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin")) + + @pytest.mark.xfail(condition=is_transformers_version(">", "4.56.2"), reason="Some import error", strict=False) + def test_no_safetensors_download_when_doing_pytorch(self): + use_safetensors = False + + with tempfile.TemporaryDirectory() as tmpdirname: + _ = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/diffusers-stable-diffusion-tiny-all", + cache_dir=tmpdirname, + use_safetensors=use_safetensors, + ) + + path = os.path.join( + tmpdirname, + "models--hf-internal-testing--diffusers-stable-diffusion-tiny-all", + "snapshots", + "07838d72e12f9bcec1375b0482b80c1d399be843", + "unet", + ) + # safetensors does not exists + assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors")) + # pytorch does + assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin")) + + def test_optional_components(self): + unet = self.dummy_cond_unet() + pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler") + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + orig_sd = StableDiffusionPipeline( + unet=unet, + scheduler=pndm, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=unet, + feature_extractor=self.dummy_extractor, + ) + sd = orig_sd + + assert sd.config.requires_safety_checker is True + + with tempfile.TemporaryDirectory() as tmpdirname: + sd.save_pretrained(tmpdirname) + + # Test that passing None works + sd = StableDiffusionPipeline.from_pretrained( + tmpdirname, feature_extractor=None, safety_checker=None, requires_safety_checker=False + ) + + assert sd.config.requires_safety_checker is False + assert sd.config.safety_checker == (None, None) + assert sd.config.feature_extractor == (None, None) + + with tempfile.TemporaryDirectory() as tmpdirname: + sd.save_pretrained(tmpdirname) + + # Test that loading previous None works + sd = StableDiffusionPipeline.from_pretrained(tmpdirname) + + assert sd.config.requires_safety_checker is False + assert sd.config.safety_checker == (None, None) + assert sd.config.feature_extractor == (None, None) + + orig_sd.save_pretrained(tmpdirname) + + # Test that loading without any directory works + shutil.rmtree(os.path.join(tmpdirname, "safety_checker")) + with open(os.path.join(tmpdirname, sd.config_name)) as f: + config = json.load(f) + config["safety_checker"] = [None, None] + with open(os.path.join(tmpdirname, sd.config_name), "w") as f: + json.dump(config, f) + + sd = StableDiffusionPipeline.from_pretrained(tmpdirname, requires_safety_checker=False) + sd.save_pretrained(tmpdirname) + sd = StableDiffusionPipeline.from_pretrained(tmpdirname) + + assert sd.config.requires_safety_checker is False + assert sd.config.safety_checker == (None, None) + assert sd.config.feature_extractor == (None, None) + + # Test that loading from deleted model index works + with open(os.path.join(tmpdirname, sd.config_name)) as f: + config = json.load(f) + del config["safety_checker"] + del config["feature_extractor"] + with open(os.path.join(tmpdirname, sd.config_name), "w") as f: + json.dump(config, f) + + sd = StableDiffusionPipeline.from_pretrained(tmpdirname) + + assert sd.config.requires_safety_checker is False + assert sd.config.safety_checker == (None, None) + assert sd.config.feature_extractor == (None, None) + + with tempfile.TemporaryDirectory() as tmpdirname: + sd.save_pretrained(tmpdirname) + + # Test that partially loading works + sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor) + + assert sd.config.requires_safety_checker is False + assert sd.config.safety_checker == (None, None) + assert sd.config.feature_extractor != (None, None) + + # Test that partially loading works + sd = StableDiffusionPipeline.from_pretrained( + tmpdirname, + feature_extractor=self.dummy_extractor, + safety_checker=unet, + requires_safety_checker=[True, True], + ) + + assert sd.config.requires_safety_checker == [True, True] + assert sd.config.safety_checker != (None, None) + assert sd.config.feature_extractor != (None, None) + + with tempfile.TemporaryDirectory() as tmpdirname: + sd.save_pretrained(tmpdirname) + sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor) + + assert sd.config.requires_safety_checker == [True, True] + assert sd.config.safety_checker != (None, None) + assert sd.config.feature_extractor != (None, None) + + def test_name_or_path(self): + model_path = "hf-internal-testing/tiny-stable-diffusion-torch" + sd = DiffusionPipeline.from_pretrained(model_path) + + assert sd.name_or_path == model_path + + with tempfile.TemporaryDirectory() as tmpdirname: + sd.save_pretrained(tmpdirname) + sd = DiffusionPipeline.from_pretrained(tmpdirname) + + assert sd.name_or_path == tmpdirname + + def test_error_no_variant_available(self): + variant = "fp16" + with self.assertRaises(ValueError) as error_context: + _ = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/diffusers-stable-diffusion-tiny-all", variant=variant + ) + + assert "but no such modeling files are available" in str(error_context.exception) + assert variant in str(error_context.exception) + + def test_pipe_to(self): + unet = self.dummy_cond_unet() + scheduler = PNDMScheduler(skip_prk_steps=True) + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + sd = StableDiffusionPipeline( + unet=unet, + scheduler=scheduler, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ) + + device_type = torch.device(torch_device).type + + sd1 = sd.to(device_type) + sd2 = sd.to(torch.device(device_type)) + sd3 = sd.to(device_type, torch.float32) + sd4 = sd.to(device=device_type) + sd5 = sd.to(torch_device=device_type) + sd6 = sd.to(device_type, dtype=torch.float32) + sd7 = sd.to(device_type, torch_dtype=torch.float32) + + assert sd1.device.type == device_type + assert sd2.device.type == device_type + assert sd3.device.type == device_type + assert sd4.device.type == device_type + assert sd5.device.type == device_type + assert sd6.device.type == device_type + assert sd7.device.type == device_type + + sd1 = sd.to(torch.float16) + sd2 = sd.to(None, torch.float16) + sd3 = sd.to(dtype=torch.float16) + sd4 = sd.to(dtype=torch.float16) + sd5 = sd.to(None, dtype=torch.float16) + sd6 = sd.to(None, torch_dtype=torch.float16) + + assert sd1.dtype == torch.float16 + assert sd2.dtype == torch.float16 + assert sd3.dtype == torch.float16 + assert sd4.dtype == torch.float16 + assert sd5.dtype == torch.float16 + assert sd6.dtype == torch.float16 + + sd1 = sd.to(device=device_type, dtype=torch.float16) + sd2 = sd.to(torch_device=device_type, torch_dtype=torch.float16) + sd3 = sd.to(device_type, torch.float16) + + assert sd1.dtype == torch.float16 + assert sd2.dtype == torch.float16 + assert sd3.dtype == torch.float16 + + assert sd1.device.type == device_type + assert sd2.device.type == device_type + assert sd3.device.type == device_type + + def test_pipe_same_device_id_offload(self): + unet = self.dummy_cond_unet() + scheduler = PNDMScheduler(skip_prk_steps=True) + vae = self.dummy_vae + bert = self.dummy_text_encoder + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + sd = StableDiffusionPipeline( + unet=unet, + scheduler=scheduler, + vae=vae, + text_encoder=bert, + tokenizer=tokenizer, + safety_checker=None, + feature_extractor=self.dummy_extractor, + ) + + # `enable_model_cpu_offload` detects device type when not passed + # `enable_model_cpu_offload` raises ValueError if detected device is `cpu` + # This test only checks whether `_offload_gpu_id` is set correctly + # So the device passed can be any supported `torch.device` type + # This allows us to keep the test under `PipelineFastTests` + sd.enable_model_cpu_offload(gpu_id=5, device="cuda") + assert sd._offload_gpu_id == 5 + sd.maybe_free_model_hooks() + assert sd._offload_gpu_id == 5 + + @parameterized.expand([torch.float32, torch.float16]) + @require_hf_hub_version_greater("0.26.5") + @require_transformers_version_greater("4.47.1") + def test_load_dduf_from_hub(self, dtype): + with tempfile.TemporaryDirectory() as tmpdir: + pipe = DiffusionPipeline.from_pretrained( + "DDUF/tiny-flux-dev-pipe-dduf", dduf_file="fluxpipeline.dduf", cache_dir=tmpdir, torch_dtype=dtype + ).to(torch_device) + out_1 = pipe(prompt="dog", num_inference_steps=5, generator=torch.manual_seed(0), output_type="np").images + + pipe.save_pretrained(tmpdir) + loaded_pipe = DiffusionPipeline.from_pretrained(tmpdir, torch_dtype=dtype).to(torch_device) + + out_2 = loaded_pipe( + prompt="dog", num_inference_steps=5, generator=torch.manual_seed(0), output_type="np" + ).images + + self.assertTrue(np.allclose(out_1, out_2, atol=1e-4, rtol=1e-4)) + + @require_hf_hub_version_greater("0.26.5") + @require_transformers_version_greater("4.47.1") + def test_load_dduf_from_hub_local_files_only(self): + with tempfile.TemporaryDirectory() as tmpdir: + pipe = DiffusionPipeline.from_pretrained( + "DDUF/tiny-flux-dev-pipe-dduf", dduf_file="fluxpipeline.dduf", cache_dir=tmpdir + ).to(torch_device) + out_1 = pipe(prompt="dog", num_inference_steps=5, generator=torch.manual_seed(0), output_type="np").images + + local_files_pipe = DiffusionPipeline.from_pretrained( + "DDUF/tiny-flux-dev-pipe-dduf", dduf_file="fluxpipeline.dduf", cache_dir=tmpdir, local_files_only=True + ).to(torch_device) + out_2 = local_files_pipe( + prompt="dog", num_inference_steps=5, generator=torch.manual_seed(0), output_type="np" + ).images + + self.assertTrue(np.allclose(out_1, out_2, atol=1e-4, rtol=1e-4)) + + def test_dduf_raises_error_with_custom_pipeline(self): + with self.assertRaises(NotImplementedError): + _ = DiffusionPipeline.from_pretrained( + "DDUF/tiny-flux-dev-pipe-dduf", dduf_file="fluxpipeline.dduf", custom_pipeline="my_pipeline" + ) + + def test_dduf_raises_error_with_connected_pipeline(self): + with self.assertRaises(NotImplementedError): + _ = DiffusionPipeline.from_pretrained( + "DDUF/tiny-flux-dev-pipe-dduf", dduf_file="fluxpipeline.dduf", load_connected_pipeline=True + ) + + @pytest.mark.xfail(condition=is_transformers_version(">", "4.56.2"), reason="Some import error", strict=False) + def test_wrong_model(self): + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + with self.assertRaises(ValueError) as error_context: + _ = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/diffusers-stable-diffusion-tiny-all", text_encoder=tokenizer + ) + + assert "is of type" in str(error_context.exception) + assert "but should be" in str(error_context.exception) + + @require_hf_hub_version_greater("0.26.5") + @require_transformers_version_greater("4.47.1") + def test_dduf_load_sharded_checkpoint_diffusion_model(self): + with tempfile.TemporaryDirectory() as tmpdir: + pipe = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-flux-dev-pipe-sharded-checkpoint-DDUF", + dduf_file="tiny-flux-dev-pipe-sharded-checkpoint.dduf", + cache_dir=tmpdir, + ).to(torch_device) + + out_1 = pipe(prompt="dog", num_inference_steps=5, generator=torch.manual_seed(0), output_type="np").images + + pipe.save_pretrained(tmpdir) + loaded_pipe = DiffusionPipeline.from_pretrained(tmpdir).to(torch_device) + + out_2 = loaded_pipe( + prompt="dog", num_inference_steps=5, generator=torch.manual_seed(0), output_type="np" + ).images + + self.assertTrue(np.allclose(out_1, out_2, atol=1e-4, rtol=1e-4)) + + +@slow +@require_torch_accelerator +class PipelineSlowTests(unittest.TestCase): + def setUp(self): + # clean up the VRAM before each test + super().setUp() + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + # clean up the VRAM after each test + super().tearDown() + gc.collect() + backend_empty_cache(torch_device) + + def test_smart_download(self): + model_id = "hf-internal-testing/unet-pipeline-dummy" + with tempfile.TemporaryDirectory() as tmpdirname: + _ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True) + local_repo_name = "--".join(["models"] + model_id.split("/")) + snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots") + snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0]) + + # inspect all downloaded files to make sure that everything is included + assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name)) + assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME)) + assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME)) + assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME)) + assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME)) + assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME)) + assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME)) + # let's make sure the super large numpy file: + # https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy + # is not downloaded, but all the expected ones + assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy")) + + def test_warning_unused_kwargs(self): + model_id = "hf-internal-testing/unet-pipeline-dummy" + logger = logging.get_logger("diffusers.pipelines") + with tempfile.TemporaryDirectory() as tmpdirname: + with CaptureLogger(logger) as cap_logger: + DiffusionPipeline.from_pretrained( + model_id, + not_used=True, + cache_dir=tmpdirname, + force_download=True, + ) + + assert ( + cap_logger.out.strip().split("\n")[-1] + == "Keyword arguments {'not_used': True} are not expected by DDPMPipeline and will be ignored." + ) + + def test_from_save_pretrained(self): + # 1. Load models + model = UNet2DModel( + block_out_channels=(32, 64), + layers_per_block=2, + sample_size=32, + in_channels=3, + out_channels=3, + down_block_types=("DownBlock2D", "AttnDownBlock2D"), + up_block_types=("AttnUpBlock2D", "UpBlock2D"), + ) + scheduler = DDPMScheduler(num_train_timesteps=10) + + ddpm = DDPMPipeline(model, scheduler) + ddpm.to(torch_device) + ddpm.set_progress_bar_config(disable=None) + + with tempfile.TemporaryDirectory() as tmpdirname: + ddpm.save_pretrained(tmpdirname) + new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) + new_ddpm.to(torch_device) + + generator = torch.Generator(device=torch_device).manual_seed(0) + image = ddpm(generator=generator, num_inference_steps=5, output_type="np").images + + generator = torch.Generator(device=torch_device).manual_seed(0) + new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="np").images + + assert np.abs(image - new_image).max() < 1e-5, "Models don't give the same forward pass" + + @is_torch_compile + @require_torch_2 + @unittest.skipIf( + get_python_version == (3, 12), + reason="Torch Dynamo isn't yet supported for Python 3.12.", + ) + def test_from_save_pretrained_dynamo(self): + torch.compiler.rest() + with torch._inductor.utils.fresh_inductor_cache(): + run_test_in_subprocess(test_case=self, target_func=_test_from_save_pretrained_dynamo, inputs=None) + + def test_from_pretrained_hub(self): + model_path = "google/ddpm-cifar10-32" + + scheduler = DDPMScheduler(num_train_timesteps=10) + + ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler) + ddpm = ddpm.to(torch_device) + ddpm.set_progress_bar_config(disable=None) + + ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler) + ddpm_from_hub = ddpm_from_hub.to(torch_device) + ddpm_from_hub.set_progress_bar_config(disable=None) + + generator = torch.Generator(device=torch_device).manual_seed(0) + image = ddpm(generator=generator, num_inference_steps=5, output_type="np").images + + generator = torch.Generator(device=torch_device).manual_seed(0) + new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="np").images + + assert np.abs(image - new_image).max() < 1e-5, "Models don't give the same forward pass" + + def test_from_pretrained_hub_pass_model(self): + model_path = "google/ddpm-cifar10-32" + + scheduler = DDPMScheduler(num_train_timesteps=10) + + # pass unet into DiffusionPipeline + unet = UNet2DModel.from_pretrained(model_path) + ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler) + ddpm_from_hub_custom_model = ddpm_from_hub_custom_model.to(torch_device) + ddpm_from_hub_custom_model.set_progress_bar_config(disable=None) + + ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler) + ddpm_from_hub = ddpm_from_hub.to(torch_device) + ddpm_from_hub_custom_model.set_progress_bar_config(disable=None) + + generator = torch.Generator(device=torch_device).manual_seed(0) + image = ddpm_from_hub_custom_model(generator=generator, num_inference_steps=5, output_type="np").images + + generator = torch.Generator(device=torch_device).manual_seed(0) + new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="np").images + + assert np.abs(image - new_image).max() < 1e-5, "Models don't give the same forward pass" + + def test_output_format(self): + model_path = "google/ddpm-cifar10-32" + + scheduler = DDIMScheduler.from_pretrained(model_path) + pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + images = pipe(output_type="np").images + assert images.shape == (1, 32, 32, 3) + assert isinstance(images, np.ndarray) + + images = pipe(output_type="pil", num_inference_steps=4).images + assert isinstance(images, list) + assert len(images) == 1 + assert isinstance(images[0], PIL.Image.Image) + + # use PIL by default + images = pipe(num_inference_steps=4).images + assert isinstance(images, list) + assert isinstance(images[0], PIL.Image.Image) + + @require_flax + def test_from_flax_from_pt(self): + pipe_pt = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe_pt.to(torch_device) + + from diffusers import FlaxStableDiffusionPipeline + + with tempfile.TemporaryDirectory() as tmpdirname: + pipe_pt.save_pretrained(tmpdirname) + + pipe_flax, params = FlaxStableDiffusionPipeline.from_pretrained( + tmpdirname, safety_checker=None, from_pt=True + ) + + with tempfile.TemporaryDirectory() as tmpdirname: + pipe_flax.save_pretrained(tmpdirname, params=params) + pipe_pt_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None, from_flax=True) + pipe_pt_2.to(torch_device) + + prompt = "Hello" + + generator = torch.manual_seed(0) + image_0 = pipe_pt( + [prompt], + generator=generator, + num_inference_steps=2, + output_type="np", + ).images[0] + + generator = torch.manual_seed(0) + image_1 = pipe_pt_2( + [prompt], + generator=generator, + num_inference_steps=2, + output_type="np", + ).images[0] + + assert np.abs(image_0 - image_1).sum() < 1e-5, "Models don't give the same forward pass" + + @require_compel + def test_weighted_prompts_compel(self): + from compel import Compel + + pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") + pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + pipe.enable_model_cpu_offload(device=torch_device) + pipe.enable_attention_slicing() + + compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder) + + prompt = "a red cat playing with a ball{}" + + prompts = [prompt.format(s) for s in ["", "++", "--"]] + + prompt_embeds = compel(prompts) + + generator = [torch.Generator(device="cpu").manual_seed(33) for _ in range(prompt_embeds.shape[0])] + + images = pipe( + prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20, output_type="np" + ).images + + for i, image in enumerate(images): + expected_image = load_numpy( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" + f"/compel/forest_{i}.npy" + ) + + assert np.abs(image - expected_image).max() < 3e-1 + + +@nightly +@require_torch_accelerator +class PipelineNightlyTests(unittest.TestCase): + def setUp(self): + # clean up the VRAM before each test + super().setUp() + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + # clean up the VRAM after each test + super().tearDown() + gc.collect() + backend_empty_cache(torch_device) + + def test_ddpm_ddim_equality_batched(self): + seed = 0 + model_id = "google/ddpm-cifar10-32" + + unet = UNet2DModel.from_pretrained(model_id) + ddpm_scheduler = DDPMScheduler() + ddim_scheduler = DDIMScheduler() + + ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler) + ddpm.to(torch_device) + ddpm.set_progress_bar_config(disable=None) + + ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler) + ddim.to(torch_device) + ddim.set_progress_bar_config(disable=None) + + generator = torch.Generator(device=torch_device).manual_seed(seed) + ddpm_images = ddpm(batch_size=2, generator=generator, output_type="np").images + + generator = torch.Generator(device=torch_device).manual_seed(seed) + ddim_images = ddim( + batch_size=2, + generator=generator, + num_inference_steps=1000, + eta=1.0, + output_type="np", + use_clipped_model_output=True, # Need this to make DDIM match DDPM + ).images + + # the values aren't exactly equal, but the images look the same visually + assert np.abs(ddpm_images - ddim_images).max() < 1e-1 + + +@slow +@require_torch_2 +@require_torch_accelerator +@require_peft_backend +@require_peft_version_greater("0.14.0") +@is_torch_compile +class TestLoraHotSwappingForPipeline(unittest.TestCase): + """Test that hotswapping does not result in recompilation in a pipeline. + + We're not extensively testing the hotswapping functionality since it is implemented in PEFT and is extensively + tested there. The goal of this test is specifically to ensure that hotswapping with diffusers does not require + recompilation. + + See + https://github.com/huggingface/peft/blob/eaab05e18d51fb4cce20a73c9acd82a00c013b83/tests/test_gpu_examples.py#L4252 + for the analogous PEFT test. + + """ + + def tearDown(self): + # It is critical that the dynamo cache is reset for each test. Otherwise, if the test re-uses the same model, + # there will be recompilation errors, as torch caches the model when run in the same process. + super().tearDown() + torch.compiler.reset() + gc.collect() + backend_empty_cache(torch_device) + + def get_unet_lora_config(self, lora_rank, lora_alpha, target_modules): + # from diffusers test_models_unet_2d_condition.py + from peft import LoraConfig + + unet_lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + target_modules=target_modules, + init_lora_weights=False, + use_dora=False, + ) + return unet_lora_config + + def get_lora_state_dicts(self, modules_to_save, adapter_name): + from peft import get_peft_model_state_dict + + state_dicts = {} + for module_name, module in modules_to_save.items(): + if module is not None: + state_dicts[f"{module_name}_lora_layers"] = get_peft_model_state_dict( + module, adapter_name=adapter_name + ) + return state_dicts + + def get_dummy_input(self): + pipeline_inputs = { + "prompt": "A painting of a squirrel eating a burger", + "num_inference_steps": 5, + "guidance_scale": 6.0, + "output_type": "np", + "return_dict": False, + } + return pipeline_inputs + + def check_pipeline_hotswap(self, do_compile, rank0, rank1, target_modules0, target_modules1=None): + """ + Check that hotswapping works on a pipeline. + + Steps: + - create 2 LoRA adapters and save them + - load the first adapter + - hotswap the second adapter + - check that the outputs are correct + - optionally compile the model + + Note: We set rank == alpha here because save_lora_adapter does not save the alpha scalings, thus the test would + fail if the values are different. Since rank != alpha does not matter for the purpose of this test, this is + fine. + """ + # create 2 adapters with different ranks and alphas + dummy_input = self.get_dummy_input() + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device) + alpha0, alpha1 = rank0, rank1 + max_rank = max([rank0, rank1]) + if target_modules1 is None: + target_modules1 = target_modules0[:] + lora_config0 = self.get_unet_lora_config(rank0, alpha0, target_modules0) + lora_config1 = self.get_unet_lora_config(rank1, alpha1, target_modules1) + + torch.manual_seed(0) + pipeline.unet.add_adapter(lora_config0, adapter_name="adapter0") + output0_before = pipeline(**dummy_input, generator=torch.manual_seed(0))[0] + + torch.manual_seed(1) + pipeline.unet.add_adapter(lora_config1, adapter_name="adapter1") + pipeline.unet.set_adapter("adapter1") + output1_before = pipeline(**dummy_input, generator=torch.manual_seed(0))[0] + + # sanity check + tol = 1e-3 + assert not np.allclose(output0_before, output1_before, atol=tol, rtol=tol) + assert not (output0_before == 0).all() + assert not (output1_before == 0).all() + + with tempfile.TemporaryDirectory() as tmp_dirname: + # save the adapter checkpoints + lora0_state_dicts = self.get_lora_state_dicts({"unet": pipeline.unet}, adapter_name="adapter0") + StableDiffusionPipeline.save_lora_weights( + save_directory=os.path.join(tmp_dirname, "adapter0"), safe_serialization=True, **lora0_state_dicts + ) + lora1_state_dicts = self.get_lora_state_dicts({"unet": pipeline.unet}, adapter_name="adapter1") + StableDiffusionPipeline.save_lora_weights( + save_directory=os.path.join(tmp_dirname, "adapter1"), safe_serialization=True, **lora1_state_dicts + ) + del pipeline + + # load the first adapter + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device) + if do_compile or (rank0 != rank1): + # no need to prepare if the model is not compiled or if the ranks are identical + pipeline.enable_lora_hotswap(target_rank=max_rank) + + file_name0 = os.path.join(tmp_dirname, "adapter0", "pytorch_lora_weights.safetensors") + file_name1 = os.path.join(tmp_dirname, "adapter1", "pytorch_lora_weights.safetensors") + + pipeline.load_lora_weights(file_name0) + if do_compile: + pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead") + + output0_after = pipeline(**dummy_input, generator=torch.manual_seed(0))[0] + + # sanity check: still same result + assert np.allclose(output0_before, output0_after, atol=tol, rtol=tol) + + # hotswap the 2nd adapter + pipeline.load_lora_weights(file_name1, hotswap=True, adapter_name="default_0") + output1_after = pipeline(**dummy_input, generator=torch.manual_seed(0))[0] + + # sanity check: since it's the same LoRA, the results should be identical + assert np.allclose(output1_before, output1_after, atol=tol, rtol=tol) + + @parameterized.expand([(11, 11), (7, 13), (13, 7)]) # important to test small to large and vice versa + def test_hotswapping_pipeline(self, rank0, rank1): + self.check_pipeline_hotswap( + do_compile=False, rank0=rank0, rank1=rank1, target_modules0=["to_q", "to_k", "to_v", "to_out.0"] + ) + + @parameterized.expand([(11, 11), (7, 13), (13, 7)]) # important to test small to large and vice versa + def test_hotswapping_compiled_pipline_linear(self, rank0, rank1): + # It's important to add this context to raise an error on recompilation + target_modules = ["to_q", "to_k", "to_v", "to_out.0"] + with torch._dynamo.config.patch(error_on_recompile=True), torch._inductor.utils.fresh_inductor_cache(): + self.check_pipeline_hotswap(do_compile=True, rank0=rank0, rank1=rank1, target_modules0=target_modules) + + @parameterized.expand([(11, 11), (7, 13), (13, 7)]) # important to test small to large and vice versa + def test_hotswapping_compiled_pipline_conv2d(self, rank0, rank1): + # It's important to add this context to raise an error on recompilation + target_modules = ["conv", "conv1", "conv2"] + with torch._dynamo.config.patch(error_on_recompile=True), torch._inductor.utils.fresh_inductor_cache(): + self.check_pipeline_hotswap(do_compile=True, rank0=rank0, rank1=rank1, target_modules0=target_modules) + + @parameterized.expand([(11, 11), (7, 13), (13, 7)]) # important to test small to large and vice versa + def test_hotswapping_compiled_pipline_both_linear_and_conv2d(self, rank0, rank1): + # It's important to add this context to raise an error on recompilation + target_modules = ["to_q", "conv"] + with torch._dynamo.config.patch(error_on_recompile=True), torch._inductor.utils.fresh_inductor_cache(): + self.check_pipeline_hotswap(do_compile=True, rank0=rank0, rank1=rank1, target_modules0=target_modules) + + def test_enable_lora_hotswap_called_after_adapter_added_raises(self): + # ensure that enable_lora_hotswap is called before loading the first adapter + lora_config = self.get_unet_lora_config(8, 8, target_modules=["to_q"]) + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device) + pipeline.unet.add_adapter(lora_config) + msg = re.escape("Call `enable_lora_hotswap` before loading the first adapter.") + with self.assertRaisesRegex(RuntimeError, msg): + pipeline.enable_lora_hotswap(target_rank=32) + + def test_enable_lora_hotswap_called_after_adapter_added_warns(self): + # ensure that enable_lora_hotswap is called before loading the first adapter + from diffusers.loaders.peft import logger + + lora_config = self.get_unet_lora_config(8, 8, target_modules=["to_q"]) + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device) + pipeline.unet.add_adapter(lora_config) + msg = ( + "It is recommended to call `enable_lora_hotswap` before loading the first adapter to avoid recompilation." + ) + with self.assertLogs(logger=logger, level="WARNING") as cm: + pipeline.enable_lora_hotswap(target_rank=32, check_compiled="warn") + assert any(msg in log for log in cm.output) + + def test_enable_lora_hotswap_called_after_adapter_added_ignore(self): + # check possibility to ignore the error/warning + lora_config = self.get_unet_lora_config(8, 8, target_modules=["to_q"]) + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device) + pipeline.unet.add_adapter(lora_config) + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") # Capture all warnings + pipeline.enable_lora_hotswap(target_rank=32, check_compiled="warn") + self.assertEqual(len(w), 0, f"Expected no warnings, but got: {[str(warn.message) for warn in w]}") + + def test_enable_lora_hotswap_wrong_check_compiled_argument_raises(self): + # check that wrong argument value raises an error + lora_config = self.get_unet_lora_config(8, 8, target_modules=["to_q"]) + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device) + pipeline.unet.add_adapter(lora_config) + msg = re.escape("check_compiles should be one of 'error', 'warn', or 'ignore', got 'wrong-argument' instead.") + with self.assertRaisesRegex(ValueError, msg): + pipeline.enable_lora_hotswap(target_rank=32, check_compiled="wrong-argument") + + def test_hotswap_second_adapter_targets_more_layers_raises(self): + # check the error and log + from diffusers.loaders.peft import logger + + # at the moment, PEFT requires the 2nd adapter to target the same or a subset of layers + target_modules0 = ["to_q"] + target_modules1 = ["to_q", "to_k"] + with self.assertRaises(RuntimeError): # peft raises RuntimeError + with self.assertLogs(logger=logger, level="ERROR") as cm: + self.check_pipeline_hotswap( + do_compile=True, rank0=8, rank1=8, target_modules0=target_modules0, target_modules1=target_modules1 + ) + assert any("Hotswapping adapter0 was unsuccessful" in log for log in cm.output) + + def test_hotswap_component_not_supported_raises(self): + # right now, not some components don't support hotswapping, e.g. the text_encoder + from peft import LoraConfig + + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device) + lora_config0 = LoraConfig(target_modules=["q_proj"]) + lora_config1 = LoraConfig(target_modules=["q_proj"]) + + pipeline.text_encoder.add_adapter(lora_config0, adapter_name="adapter0") + pipeline.text_encoder.add_adapter(lora_config1, adapter_name="adapter1") + + with tempfile.TemporaryDirectory() as tmp_dirname: + # save the adapter checkpoints + lora0_state_dicts = self.get_lora_state_dicts( + {"text_encoder": pipeline.text_encoder}, adapter_name="adapter0" + ) + StableDiffusionPipeline.save_lora_weights( + save_directory=os.path.join(tmp_dirname, "adapter0"), safe_serialization=True, **lora0_state_dicts + ) + lora1_state_dicts = self.get_lora_state_dicts( + {"text_encoder": pipeline.text_encoder}, adapter_name="adapter1" + ) + StableDiffusionPipeline.save_lora_weights( + save_directory=os.path.join(tmp_dirname, "adapter1"), safe_serialization=True, **lora1_state_dicts + ) + del pipeline + + # load the first adapter + pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe").to(torch_device) + file_name0 = os.path.join(tmp_dirname, "adapter0", "pytorch_lora_weights.safetensors") + file_name1 = os.path.join(tmp_dirname, "adapter1", "pytorch_lora_weights.safetensors") + + pipeline.load_lora_weights(file_name0) + msg = re.escape( + "At the moment, hotswapping is not supported for text encoders, please pass `hotswap=False`" + ) + with self.assertRaisesRegex(ValueError, msg): + pipeline.load_lora_weights(file_name1, hotswap=True, adapter_name="default_0") diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_auto.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_auto.py new file mode 100644 index 0000000000000000000000000000000000000000..f3c639c367f73517a1d9f0c2b051d51d17e961c3 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_auto.py @@ -0,0 +1,575 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import os +import shutil +import unittest +from collections import OrderedDict +from pathlib import Path + +import torch +from transformers import CLIPVisionConfig, CLIPVisionModelWithProjection + +from diffusers import ( + AutoPipelineForImage2Image, + AutoPipelineForInpainting, + AutoPipelineForText2Image, + ControlNetModel, + DiffusionPipeline, +) +from diffusers.pipelines.auto_pipeline import ( + AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, + AUTO_INPAINT_PIPELINES_MAPPING, + AUTO_TEXT2IMAGE_PIPELINES_MAPPING, +) + +from ..testing_utils import slow + + +PRETRAINED_MODEL_REPO_MAPPING = OrderedDict( + [ + ("stable-diffusion", "stable-diffusion-v1-5/stable-diffusion-v1-5"), + ("if", "DeepFloyd/IF-I-XL-v1.0"), + ("kandinsky", "kandinsky-community/kandinsky-2-1"), + ("kandinsky22", "kandinsky-community/kandinsky-2-2-decoder"), + ] +) + + +class AutoPipelineFastTest(unittest.TestCase): + @property + def dummy_image_encoder(self): + torch.manual_seed(0) + config = CLIPVisionConfig( + hidden_size=1, + projection_dim=1, + num_hidden_layers=1, + num_attention_heads=1, + image_size=1, + intermediate_size=1, + patch_size=1, + ) + return CLIPVisionModelWithProjection(config) + + def test_from_pipe_consistent(self): + pipe = AutoPipelineForText2Image.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False + ) + original_config = dict(pipe.config) + + pipe = AutoPipelineForImage2Image.from_pipe(pipe) + assert dict(pipe.config) == original_config + + pipe = AutoPipelineForText2Image.from_pipe(pipe) + assert dict(pipe.config) == original_config + + def test_from_pipe_override(self): + pipe = AutoPipelineForText2Image.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False + ) + + pipe = AutoPipelineForImage2Image.from_pipe(pipe, requires_safety_checker=True) + assert pipe.config.requires_safety_checker is True + + pipe = AutoPipelineForText2Image.from_pipe(pipe, requires_safety_checker=True) + assert pipe.config.requires_safety_checker is True + + def test_from_pipe_consistent_sdxl(self): + pipe = AutoPipelineForImage2Image.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-xl-pipe", + requires_aesthetics_score=True, + force_zeros_for_empty_prompt=False, + ) + + original_config = dict(pipe.config) + + pipe = AutoPipelineForText2Image.from_pipe(pipe) + pipe = AutoPipelineForImage2Image.from_pipe(pipe) + + assert dict(pipe.config) == original_config + + def test_kwargs_local_files_only(self): + repo = "hf-internal-testing/tiny-stable-diffusion-torch" + tmpdirname = DiffusionPipeline.download(repo) + tmpdirname = Path(tmpdirname) + + # edit commit_id to so that it's not the latest commit + commit_id = tmpdirname.name + new_commit_id = commit_id + "hug" + + ref_dir = tmpdirname.parent.parent / "refs/main" + with open(ref_dir, "w") as f: + f.write(new_commit_id) + + new_tmpdirname = tmpdirname.parent / new_commit_id + os.rename(tmpdirname, new_tmpdirname) + + try: + AutoPipelineForText2Image.from_pretrained(repo, local_files_only=True) + except OSError: + assert False, "not able to load local files" + + shutil.rmtree(tmpdirname.parent.parent) + + def test_from_pretrained_text2img(self): + repo = "hf-internal-testing/tiny-stable-diffusion-xl-pipe" + pipe = AutoPipelineForText2Image.from_pretrained(repo) + assert pipe.__class__.__name__ == "StableDiffusionXLPipeline" + + controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet") + pipe_control = AutoPipelineForText2Image.from_pretrained(repo, controlnet=controlnet) + assert pipe_control.__class__.__name__ == "StableDiffusionXLControlNetPipeline" + + pipe_pag = AutoPipelineForText2Image.from_pretrained(repo, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGPipeline" + + pipe_control_pag = AutoPipelineForText2Image.from_pretrained(repo, controlnet=controlnet, enable_pag=True) + assert pipe_control_pag.__class__.__name__ == "StableDiffusionXLControlNetPAGPipeline" + + def test_from_pipe_pag_text2img(self): + # test from StableDiffusionXLPipeline + pipe = AutoPipelineForText2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe") + controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet") + + # - test `enable_pag` flag + pipe_pag = AutoPipelineForText2Image.from_pipe(pipe, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGPipeline" + assert "controlnet" not in pipe_pag.components + + pipe = AutoPipelineForText2Image.from_pipe(pipe, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLPipeline" + assert "controlnet" not in pipe.components + + # - test `enabe_pag` + `controlnet` flag + pipe_control_pag = AutoPipelineForText2Image.from_pipe(pipe, controlnet=controlnet, enable_pag=True) + assert pipe_control_pag.__class__.__name__ == "StableDiffusionXLControlNetPAGPipeline" + assert "controlnet" in pipe_control_pag.components + + pipe_control = AutoPipelineForText2Image.from_pipe(pipe, controlnet=controlnet, enable_pag=False) + assert pipe_control.__class__.__name__ == "StableDiffusionXLControlNetPipeline" + assert "controlnet" in pipe_control.components + + pipe_pag = AutoPipelineForText2Image.from_pipe(pipe, controlnet=None, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGPipeline" + assert "controlnet" not in pipe_pag.components + + pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=None, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLPipeline" + assert "controlnet" not in pipe.components + + # test from StableDiffusionXLControlNetPipeline + # - test `enable_pag` flag + pipe_control_pag = AutoPipelineForText2Image.from_pipe(pipe_control, enable_pag=True) + assert pipe_control_pag.__class__.__name__ == "StableDiffusionXLControlNetPAGPipeline" + assert "controlnet" in pipe_control_pag.components + + pipe_control = AutoPipelineForText2Image.from_pipe(pipe_control, enable_pag=False) + assert pipe_control.__class__.__name__ == "StableDiffusionXLControlNetPipeline" + assert "controlnet" in pipe_control.components + + # - test `enable_pag` + `controlnet` flag + pipe_control_pag = AutoPipelineForText2Image.from_pipe(pipe_control, controlnet=controlnet, enable_pag=True) + assert pipe_control_pag.__class__.__name__ == "StableDiffusionXLControlNetPAGPipeline" + assert "controlnet" in pipe_control_pag.components + + pipe_control = AutoPipelineForText2Image.from_pipe(pipe_control, controlnet=controlnet, enable_pag=False) + assert pipe_control.__class__.__name__ == "StableDiffusionXLControlNetPipeline" + assert "controlnet" in pipe_control.components + + pipe_pag = AutoPipelineForText2Image.from_pipe(pipe_control, controlnet=None, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGPipeline" + assert "controlnet" not in pipe_pag.components + + pipe = AutoPipelineForText2Image.from_pipe(pipe_control, controlnet=None, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLPipeline" + assert "controlnet" not in pipe.components + + # test from StableDiffusionXLControlNetPAGPipeline + # - test `enable_pag` flag + pipe_control_pag = AutoPipelineForText2Image.from_pipe(pipe_control_pag, enable_pag=True) + assert pipe_control_pag.__class__.__name__ == "StableDiffusionXLControlNetPAGPipeline" + assert "controlnet" in pipe_control_pag.components + + pipe_control = AutoPipelineForText2Image.from_pipe(pipe_control_pag, enable_pag=False) + assert pipe_control.__class__.__name__ == "StableDiffusionXLControlNetPipeline" + assert "controlnet" in pipe_control.components + + # - test `enable_pag` + `controlnet` flag + pipe_control_pag = AutoPipelineForText2Image.from_pipe( + pipe_control_pag, controlnet=controlnet, enable_pag=True + ) + assert pipe_control_pag.__class__.__name__ == "StableDiffusionXLControlNetPAGPipeline" + assert "controlnet" in pipe_control_pag.components + + pipe_control = AutoPipelineForText2Image.from_pipe(pipe_control_pag, controlnet=controlnet, enable_pag=False) + assert pipe_control.__class__.__name__ == "StableDiffusionXLControlNetPipeline" + assert "controlnet" in pipe_control.components + + pipe_pag = AutoPipelineForText2Image.from_pipe(pipe_control_pag, controlnet=None, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGPipeline" + assert "controlnet" not in pipe_pag.components + + pipe = AutoPipelineForText2Image.from_pipe(pipe_control_pag, controlnet=None, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLPipeline" + assert "controlnet" not in pipe.components + + pipe = AutoPipelineForText2Image.from_pipe(pipe_control_pag, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLControlNetPipeline" + assert "controlnet" in pipe.components + + def test_from_pretrained_img2img(self): + repo = "hf-internal-testing/tiny-stable-diffusion-xl-pipe" + + pipe = AutoPipelineForImage2Image.from_pretrained(repo) + assert pipe.__class__.__name__ == "StableDiffusionXLImg2ImgPipeline" + + controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet") + pipe_control = AutoPipelineForImage2Image.from_pretrained(repo, controlnet=controlnet) + assert pipe_control.__class__.__name__ == "StableDiffusionXLControlNetImg2ImgPipeline" + + pipe_pag = AutoPipelineForImage2Image.from_pretrained(repo, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGImg2ImgPipeline" + + pipe_control_pag = AutoPipelineForImage2Image.from_pretrained(repo, controlnet=controlnet, enable_pag=True) + assert pipe_control_pag.__class__.__name__ == "StableDiffusionXLControlNetPAGImg2ImgPipeline" + + def test_from_pretrained_img2img_refiner(self): + repo = "hf-internal-testing/tiny-stable-diffusion-xl-refiner-pipe" + + pipe = AutoPipelineForImage2Image.from_pretrained(repo) + assert pipe.__class__.__name__ == "StableDiffusionXLImg2ImgPipeline" + + controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet") + pipe_control = AutoPipelineForImage2Image.from_pretrained(repo, controlnet=controlnet) + assert pipe_control.__class__.__name__ == "StableDiffusionXLControlNetImg2ImgPipeline" + + pipe_pag = AutoPipelineForImage2Image.from_pretrained(repo, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGImg2ImgPipeline" + + pipe_control_pag = AutoPipelineForImage2Image.from_pretrained(repo, controlnet=controlnet, enable_pag=True) + assert pipe_control_pag.__class__.__name__ == "StableDiffusionXLControlNetPAGImg2ImgPipeline" + + def test_from_pipe_pag_img2img(self): + # test from tableDiffusionXLPAGImg2ImgPipeline + pipe = AutoPipelineForImage2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe") + # - test `enable_pag` flag + pipe_pag = AutoPipelineForImage2Image.from_pipe(pipe, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGImg2ImgPipeline" + + pipe = AutoPipelineForImage2Image.from_pipe(pipe, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLImg2ImgPipeline" + + # testing from StableDiffusionXLPAGImg2ImgPipeline + # - test `enable_pag` flag + pipe_pag = AutoPipelineForImage2Image.from_pipe(pipe_pag, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGImg2ImgPipeline" + + pipe = AutoPipelineForImage2Image.from_pipe(pipe_pag, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLImg2ImgPipeline" + + def test_from_pretrained_inpaint(self): + repo = "hf-internal-testing/tiny-stable-diffusion-xl-pipe" + + pipe = AutoPipelineForInpainting.from_pretrained(repo) + assert pipe.__class__.__name__ == "StableDiffusionXLInpaintPipeline" + + pipe_pag = AutoPipelineForInpainting.from_pretrained(repo, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGInpaintPipeline" + + def test_from_pretrained_inpaint_from_inpaint(self): + repo = "hf-internal-testing/tiny-stable-diffusion-xl-inpaint-pipe" + + pipe = AutoPipelineForInpainting.from_pretrained(repo) + assert pipe.__class__.__name__ == "StableDiffusionXLInpaintPipeline" + + # make sure you can use pag with inpaint-specific pipeline + pipe = AutoPipelineForInpainting.from_pretrained(repo, enable_pag=True) + assert pipe.__class__.__name__ == "StableDiffusionXLPAGInpaintPipeline" + + def test_from_pipe_pag_inpaint(self): + # test from tableDiffusionXLPAGInpaintPipeline + pipe = AutoPipelineForInpainting.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe") + # - test `enable_pag` flag + pipe_pag = AutoPipelineForInpainting.from_pipe(pipe, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGInpaintPipeline" + + pipe = AutoPipelineForInpainting.from_pipe(pipe, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLInpaintPipeline" + + # testing from StableDiffusionXLPAGInpaintPipeline + # - test `enable_pag` flag + pipe_pag = AutoPipelineForInpainting.from_pipe(pipe_pag, enable_pag=True) + assert pipe_pag.__class__.__name__ == "StableDiffusionXLPAGInpaintPipeline" + + pipe = AutoPipelineForInpainting.from_pipe(pipe_pag, enable_pag=False) + assert pipe.__class__.__name__ == "StableDiffusionXLInpaintPipeline" + + def test_from_pipe_pag_new_task(self): + # for from_pipe_new_task we only need to make sure it can map to the same pipeline from a different task, + # i.e. no need to test `enable_pag` + `controlnet` flag because it is already tested in `test_from_pipe_pag_text2img` and `test_from_pipe_pag_inpaint`etc + pipe_pag_text2img = AutoPipelineForText2Image.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-xl-pipe", enable_pag=True + ) + + # text2img pag -> inpaint pag + pipe_pag_inpaint = AutoPipelineForInpainting.from_pipe(pipe_pag_text2img) + assert pipe_pag_inpaint.__class__.__name__ == "StableDiffusionXLPAGInpaintPipeline" + # text2img pag -> img2img pag + pipe_pag_img2img = AutoPipelineForImage2Image.from_pipe(pipe_pag_text2img) + assert pipe_pag_img2img.__class__.__name__ == "StableDiffusionXLPAGImg2ImgPipeline" + + # inpaint pag -> text2img pag + pipe_pag_text2img = AutoPipelineForText2Image.from_pipe(pipe_pag_inpaint) + assert pipe_pag_text2img.__class__.__name__ == "StableDiffusionXLPAGPipeline" + # inpaint pag -> img2img pag + pipe_pag_img2img = AutoPipelineForImage2Image.from_pipe(pipe_pag_inpaint) + assert pipe_pag_img2img.__class__.__name__ == "StableDiffusionXLPAGImg2ImgPipeline" + + # img2img pag -> text2img pag + pipe_pag_text2img = AutoPipelineForText2Image.from_pipe(pipe_pag_img2img) + assert pipe_pag_text2img.__class__.__name__ == "StableDiffusionXLPAGPipeline" + # img2img pag -> inpaint pag + pipe_pag_inpaint = AutoPipelineForInpainting.from_pipe(pipe_pag_img2img) + assert pipe_pag_inpaint.__class__.__name__ == "StableDiffusionXLPAGInpaintPipeline" + + def test_from_pipe_controlnet_text2img(self): + pipe = AutoPipelineForText2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe") + controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet") + + pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=controlnet) + assert pipe.__class__.__name__ == "StableDiffusionControlNetPipeline" + assert "controlnet" in pipe.components + + pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=None) + assert pipe.__class__.__name__ == "StableDiffusionPipeline" + assert "controlnet" not in pipe.components + + def test_from_pipe_controlnet_img2img(self): + pipe = AutoPipelineForImage2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe") + controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet") + + pipe = AutoPipelineForImage2Image.from_pipe(pipe, controlnet=controlnet) + assert pipe.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline" + assert "controlnet" in pipe.components + + pipe = AutoPipelineForImage2Image.from_pipe(pipe, controlnet=None) + assert pipe.__class__.__name__ == "StableDiffusionImg2ImgPipeline" + assert "controlnet" not in pipe.components + + def test_from_pipe_controlnet_inpaint(self): + pipe = AutoPipelineForInpainting.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") + controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet") + + pipe = AutoPipelineForInpainting.from_pipe(pipe, controlnet=controlnet) + assert pipe.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline" + assert "controlnet" in pipe.components + + pipe = AutoPipelineForInpainting.from_pipe(pipe, controlnet=None) + assert pipe.__class__.__name__ == "StableDiffusionInpaintPipeline" + assert "controlnet" not in pipe.components + + def test_from_pipe_controlnet_new_task(self): + pipe_text2img = AutoPipelineForText2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") + controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet") + + pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_text2img, controlnet=controlnet) + assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline" + assert "controlnet" in pipe_control_img2img.components + + pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img, controlnet=None) + assert pipe_inpaint.__class__.__name__ == "StableDiffusionInpaintPipeline" + assert "controlnet" not in pipe_inpaint.components + + # testing `from_pipe` for text2img controlnet + ## 1. from a different controlnet pipe, without controlnet argument + pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_img2img) + assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline" + assert "controlnet" in pipe_control_text2img.components + + ## 2. from a different controlnet pipe, with controlnet argument + pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_img2img, controlnet=controlnet) + assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline" + assert "controlnet" in pipe_control_text2img.components + + ## 3. from same controlnet pipeline class, with a different controlnet component + pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_text2img, controlnet=controlnet) + assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline" + assert "controlnet" in pipe_control_text2img.components + + # testing from_pipe for inpainting + ## 1. from a different controlnet pipeline class + pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img) + assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline" + assert "controlnet" in pipe_control_inpaint.components + + ## from a different controlnet pipe, with a different controlnet + pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img, controlnet=controlnet) + assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline" + assert "controlnet" in pipe_control_inpaint.components + + ## from same controlnet pipe, with a different controlnet + pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_inpaint, controlnet=controlnet) + assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline" + assert "controlnet" in pipe_control_inpaint.components + + # testing from_pipe from img2img controlnet + ## from a different controlnet pipe, without controlnet argument + pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_text2img) + assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline" + assert "controlnet" in pipe_control_img2img.components + + # from a different controlnet pipe, with a different controlnet component + pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_text2img, controlnet=controlnet) + assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline" + assert "controlnet" in pipe_control_img2img.components + + # from same controlnet pipeline class, with a different controlnet + pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_img2img, controlnet=controlnet) + assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline" + assert "controlnet" in pipe_control_img2img.components + + def test_from_pipe_optional_components(self): + image_encoder = self.dummy_image_encoder + + pipe = AutoPipelineForText2Image.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-pipe", + image_encoder=image_encoder, + ) + + pipe = AutoPipelineForImage2Image.from_pipe(pipe) + assert pipe.image_encoder is not None + + pipe = AutoPipelineForText2Image.from_pipe(pipe, image_encoder=None) + assert pipe.image_encoder is None + + +@slow +class AutoPipelineIntegrationTest(unittest.TestCase): + def test_pipe_auto(self): + for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items(): + # test txt2img + pipe_txt2img = AutoPipelineForText2Image.from_pretrained( + model_repo, variant="fp16", torch_dtype=torch.float16 + ) + self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name]) + + pipe_to = AutoPipelineForText2Image.from_pipe(pipe_txt2img) + self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name]) + + pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_txt2img) + self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name]) + + if "kandinsky" not in model_name: + pipe_to = AutoPipelineForInpainting.from_pipe(pipe_txt2img) + self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name]) + + del pipe_txt2img, pipe_to + gc.collect() + + # test img2img + + pipe_img2img = AutoPipelineForImage2Image.from_pretrained( + model_repo, variant="fp16", torch_dtype=torch.float16 + ) + self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name]) + + pipe_to = AutoPipelineForText2Image.from_pipe(pipe_img2img) + self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name]) + + pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_img2img) + self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name]) + + if "kandinsky" not in model_name: + pipe_to = AutoPipelineForInpainting.from_pipe(pipe_img2img) + self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name]) + + del pipe_img2img, pipe_to + gc.collect() + + # test inpaint + + if "kandinsky" not in model_name: + pipe_inpaint = AutoPipelineForInpainting.from_pretrained( + model_repo, variant="fp16", torch_dtype=torch.float16 + ) + self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING[model_name]) + + pipe_to = AutoPipelineForText2Image.from_pipe(pipe_inpaint) + self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name]) + + pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_inpaint) + self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name]) + + pipe_to = AutoPipelineForInpainting.from_pipe(pipe_inpaint) + self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name]) + + del pipe_inpaint, pipe_to + gc.collect() + + def test_from_pipe_consistent(self): + for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items(): + if model_name in ["kandinsky", "kandinsky22"]: + auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image] + else: + auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image, AutoPipelineForInpainting] + + # test from_pretrained + for pipe_from_class in auto_pipes: + pipe_from = pipe_from_class.from_pretrained(model_repo, variant="fp16", torch_dtype=torch.float16) + pipe_from_config = dict(pipe_from.config) + + for pipe_to_class in auto_pipes: + pipe_to = pipe_to_class.from_pipe(pipe_from) + self.assertEqual(dict(pipe_to.config), pipe_from_config) + + del pipe_from, pipe_to + gc.collect() + + def test_controlnet(self): + # test from_pretrained + model_repo = "stable-diffusion-v1-5/stable-diffusion-v1-5" + controlnet_repo = "lllyasviel/sd-controlnet-canny" + + controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16) + + pipe_txt2img = AutoPipelineForText2Image.from_pretrained( + model_repo, controlnet=controlnet, torch_dtype=torch.float16 + ) + self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"]) + + pipe_img2img = AutoPipelineForImage2Image.from_pretrained( + model_repo, controlnet=controlnet, torch_dtype=torch.float16 + ) + self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"]) + + pipe_inpaint = AutoPipelineForInpainting.from_pretrained( + model_repo, controlnet=controlnet, torch_dtype=torch.float16 + ) + self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"]) + + # test from_pipe + for pipe_from in [pipe_txt2img, pipe_img2img, pipe_inpaint]: + pipe_to = AutoPipelineForText2Image.from_pipe(pipe_from) + self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"]) + self.assertEqual(dict(pipe_to.config), dict(pipe_txt2img.config)) + + pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_from) + self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"]) + self.assertEqual(dict(pipe_to.config), dict(pipe_img2img.config)) + + pipe_to = AutoPipelineForInpainting.from_pipe(pipe_from) + self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"]) + self.assertEqual(dict(pipe_to.config), dict(pipe_inpaint.config)) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_combined.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_combined.py new file mode 100644 index 0000000000000000000000000000000000000000..fffc053bae3f9466fab4f9938df9cccd58c2b66f --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_combined.py @@ -0,0 +1,128 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import torch +from huggingface_hub import ModelCard + +from diffusers import ( + DDPMScheduler, + DiffusionPipeline, + KandinskyV22CombinedPipeline, + KandinskyV22Pipeline, + KandinskyV22PriorPipeline, +) +from diffusers.pipelines.pipeline_utils import CONNECTED_PIPES_KEYS + + +def state_dicts_almost_equal(sd1, sd2): + sd1 = dict(sorted(sd1.items())) + sd2 = dict(sorted(sd2.items())) + + models_are_equal = True + for ten1, ten2 in zip(sd1.values(), sd2.values()): + if (ten1 - ten2).abs().sum() > 1e-3: + models_are_equal = False + + return models_are_equal + + +class CombinedPipelineFastTest(unittest.TestCase): + def modelcard_has_connected_pipeline(self, model_id): + modelcard = ModelCard.load(model_id) + connected_pipes = {prefix: getattr(modelcard.data, prefix, [None])[0] for prefix in CONNECTED_PIPES_KEYS} + connected_pipes = {k: v for k, v in connected_pipes.items() if v is not None} + + return len(connected_pipes) > 0 + + def test_correct_modelcard_format(self): + # hf-internal-testing/tiny-random-kandinsky-v22-prior has no metadata + assert not self.modelcard_has_connected_pipeline("hf-internal-testing/tiny-random-kandinsky-v22-prior") + + # see https://huggingface.co/hf-internal-testing/tiny-random-kandinsky-v22-decoder/blob/8baff9897c6be017013e21b5c562e5a381646c7e/README.md?code=true#L2 + assert self.modelcard_has_connected_pipeline("hf-internal-testing/tiny-random-kandinsky-v22-decoder") + + def test_load_connected_checkpoint_when_specified(self): + pipeline_prior = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-prior") + pipeline_prior_connected = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-random-kandinsky-v22-prior", load_connected_pipeline=True + ) + + # Passing `load_connected_pipeline` to prior is a no-op as the pipeline has no connected pipeline + assert pipeline_prior.__class__ == pipeline_prior_connected.__class__ + + pipeline = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-decoder") + pipeline_connected = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-random-kandinsky-v22-decoder", load_connected_pipeline=True + ) + + # Passing `load_connected_pipeline` to decoder loads the combined pipeline + assert pipeline.__class__ != pipeline_connected.__class__ + assert pipeline.__class__ == KandinskyV22Pipeline + assert pipeline_connected.__class__ == KandinskyV22CombinedPipeline + + # check that loaded components match prior and decoder components + assert set(pipeline_connected.components.keys()) == set( + ["prior_" + k for k in pipeline_prior.components.keys()] + list(pipeline.components.keys()) + ) + + def test_load_connected_checkpoint_default(self): + prior = KandinskyV22PriorPipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-prior") + decoder = KandinskyV22Pipeline.from_pretrained("hf-internal-testing/tiny-random-kandinsky-v22-decoder") + + # check that combined pipeline loads both prior & decoder because of + # https://huggingface.co/hf-internal-testing/tiny-random-kandinsky-v22-decoder/blob/8baff9897c6be017013e21b5c562e5a381646c7e/README.md?code=true#L3 + assert ( + KandinskyV22CombinedPipeline._load_connected_pipes + ) # combined pipelines will download more checkpoints that just the one specified + pipeline = KandinskyV22CombinedPipeline.from_pretrained( + "hf-internal-testing/tiny-random-kandinsky-v22-decoder" + ) + + prior_comps = prior.components + decoder_comps = decoder.components + for k, component in pipeline.components.items(): + if k.startswith("prior_"): + k = k[6:] + comp = prior_comps[k] + else: + comp = decoder_comps[k] + + if isinstance(component, torch.nn.Module): + assert state_dicts_almost_equal(component.state_dict(), comp.state_dict()) + elif hasattr(component, "config"): + assert dict(component.config) == dict(comp.config) + else: + assert component.__class__ == comp.__class__ + + def test_load_connected_checkpoint_with_passed_obj(self): + pipeline = KandinskyV22CombinedPipeline.from_pretrained( + "hf-internal-testing/tiny-random-kandinsky-v22-decoder" + ) + prior_scheduler = DDPMScheduler.from_config(pipeline.prior_scheduler.config) + scheduler = DDPMScheduler.from_config(pipeline.scheduler.config) + + # make sure we pass a different scheduler and prior_scheduler + assert pipeline.prior_scheduler.__class__ != prior_scheduler.__class__ + assert pipeline.scheduler.__class__ != scheduler.__class__ + + pipeline_new = KandinskyV22CombinedPipeline.from_pretrained( + "hf-internal-testing/tiny-random-kandinsky-v22-decoder", + prior_scheduler=prior_scheduler, + scheduler=scheduler, + ) + assert dict(pipeline_new.prior_scheduler.config) == dict(prior_scheduler.config) + assert dict(pipeline_new.scheduler.config) == dict(scheduler.config) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_common.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_common.py new file mode 100644 index 0000000000000000000000000000000000000000..4d9d1717ba8641c921d1f22bba7e8ad3cc5c3f18 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_common.py @@ -0,0 +1,3085 @@ +import gc +import inspect +import json +import os +import tempfile +import unittest +import uuid +from typing import Any, Callable, Dict + +import numpy as np +import PIL.Image +import pytest +import torch +import torch.nn as nn +from huggingface_hub import ModelCard, delete_repo +from huggingface_hub.utils import is_jinja_available +from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer + +import diffusers +from diffusers import ( + AsymmetricAutoencoderKL, + AutoencoderKL, + AutoencoderTiny, + ConsistencyDecoderVAE, + DDIMScheduler, + DiffusionPipeline, + FasterCacheConfig, + KolorsPipeline, + PyramidAttentionBroadcastConfig, + StableDiffusionPipeline, + StableDiffusionXLPipeline, + UNet2DConditionModel, + apply_faster_cache, +) +from diffusers.hooks import apply_group_offloading +from diffusers.hooks.faster_cache import FasterCacheBlockHook, FasterCacheDenoiserHook +from diffusers.hooks.first_block_cache import FirstBlockCacheConfig +from diffusers.hooks.mag_cache import MagCacheConfig +from diffusers.hooks.pyramid_attention_broadcast import PyramidAttentionBroadcastHook +from diffusers.hooks.taylorseer_cache import TaylorSeerCacheConfig +from diffusers.image_processor import VaeImageProcessor +from diffusers.loaders import FluxIPAdapterMixin, IPAdapterMixin +from diffusers.models.attention import AttentionModuleMixin +from diffusers.models.attention_processor import AttnProcessor +from diffusers.models.controlnets.controlnet_xs import UNetControlNetXSModel +from diffusers.models.unets.unet_3d_condition import UNet3DConditionModel +from diffusers.models.unets.unet_i2vgen_xl import I2VGenXLUNet +from diffusers.models.unets.unet_motion_model import UNetMotionModel +from diffusers.pipelines.pipeline_utils import StableDiffusionMixin +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import logging +from diffusers.utils.import_utils import is_xformers_available +from diffusers.utils.source_code_parsing_utils import ReturnNameVisitor + +from ..models.autoencoders.vae import ( + get_asym_autoencoder_kl_config, + get_autoencoder_kl_config, + get_autoencoder_tiny_config, + get_consistency_vae_config, +) +from ..models.transformers.test_models_transformer_flux import create_flux_ip_adapter_state_dict +from ..models.unets.test_models_unet_2d_condition import ( + create_ip_adapter_faceid_state_dict, + create_ip_adapter_state_dict, +) +from ..others.test_utils import TOKEN, USER, is_staging_test +from ..testing_utils import ( + CaptureLogger, + backend_empty_cache, + numpy_cosine_similarity_distance, + require_accelerate_version_greater, + require_accelerator, + require_hf_hub_version_greater, + require_torch, + require_torch_accelerator, + require_transformers_version_greater, + skip_mps, + torch_device, +) + + +def to_np(tensor): + if isinstance(tensor, torch.Tensor): + tensor = tensor.detach().cpu().numpy() + + return tensor + + +def check_same_shape(tensor_list): + shapes = [tensor.shape for tensor in tensor_list] + return all(shape == shapes[0] for shape in shapes[1:]) + + +def check_qkv_fusion_matches_attn_procs_length(model, original_attn_processors): + current_attn_processors = model.attn_processors + return len(current_attn_processors) == len(original_attn_processors) + + +def check_qkv_fusion_processors_exist(model): + current_attn_processors = model.attn_processors + proc_names = [v.__class__.__name__ for _, v in current_attn_processors.items()] + return all(p.startswith("Fused") for p in proc_names) + + +def check_qkv_fused_layers_exist(model, layer_names): + is_fused_submodules = [] + for submodule in model.modules(): + if not isinstance(submodule, AttentionModuleMixin) or not submodule._supports_qkv_fusion: + continue + is_fused_attribute_set = submodule.fused_projections + is_fused_layer = True + for layer in layer_names: + is_fused_layer = is_fused_layer and getattr(submodule, layer, None) is not None + is_fused = is_fused_attribute_set and is_fused_layer + is_fused_submodules.append(is_fused) + return all(is_fused_submodules) + + +class SDFunctionTesterMixin: + """ + This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. + It provides a set of common tests for PyTorch pipeline that inherit from StableDiffusionMixin, e.g. vae_slicing, vae_tiling, freeu, etc. + """ + + def test_vae_slicing(self, image_count=4): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + # components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config) + pipe = self.pipeline_class(**components) + pipe = pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs["prompt"] = [inputs["prompt"]] * image_count + if "image" in inputs: # fix batch size mismatch in I2V_Gen pipeline + inputs["image"] = [inputs["image"]] * image_count + output_1 = pipe(**inputs) + + # make sure sliced vae decode yields the same result + pipe.enable_vae_slicing() + inputs = self.get_dummy_inputs(device) + inputs["prompt"] = [inputs["prompt"]] * image_count + if "image" in inputs: + inputs["image"] = [inputs["image"]] * image_count + inputs["return_dict"] = False + output_2 = pipe(**inputs) + + assert np.abs(output_2[0].flatten() - output_1[0].flatten()).max() < 1e-2 + + def test_vae_tiling(self): + components = self.get_dummy_components() + + # make sure here that pndm scheduler skips prk + if "safety_checker" in components: + components["safety_checker"] = None + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + inputs["return_dict"] = False + + # Test that tiled decode at 512x512 yields the same result as the non-tiled decode + output_1 = pipe(**inputs)[0] + + # make sure tiled vae decode yields the same result + pipe.enable_vae_tiling() + inputs = self.get_dummy_inputs(torch_device) + inputs["return_dict"] = False + output_2 = pipe(**inputs)[0] + + assert np.abs(to_np(output_2) - to_np(output_1)).max() < 5e-1 + + # test that tiled decode works with various shapes + shapes = [(1, 4, 73, 97), (1, 4, 65, 49)] + with torch.no_grad(): + for shape in shapes: + zeros = torch.zeros(shape).to(torch_device) + pipe.vae.decode(zeros) + + # MPS currently doesn't support ComplexFloats, which are required for FreeU - see https://github.com/huggingface/diffusers/issues/7569. + @skip_mps + def test_freeu(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + # Normal inference + inputs = self.get_dummy_inputs(torch_device) + inputs["return_dict"] = False + inputs["output_type"] = "np" + output = pipe(**inputs)[0] + + # FreeU-enabled inference + pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4) + inputs = self.get_dummy_inputs(torch_device) + inputs["return_dict"] = False + inputs["output_type"] = "np" + output_freeu = pipe(**inputs)[0] + + # FreeU-disabled inference + pipe.disable_freeu() + freeu_keys = {"s1", "s2", "b1", "b2"} + for upsample_block in pipe.unet.up_blocks: + for key in freeu_keys: + assert getattr(upsample_block, key) is None, f"Disabling of FreeU should have set {key} to None." + + inputs = self.get_dummy_inputs(torch_device) + inputs["return_dict"] = False + inputs["output_type"] = "np" + output_no_freeu = pipe(**inputs)[0] + + assert not np.allclose(output[0, -3:, -3:, -1], output_freeu[0, -3:, -3:, -1]), ( + "Enabling of FreeU should lead to different results." + ) + assert np.allclose(output, output_no_freeu, atol=1e-2), ( + f"Disabling of FreeU should lead to results similar to the default pipeline results but Max Abs Error={np.abs(output_no_freeu - output).max()}." + ) + + def test_fused_qkv_projections(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs["return_dict"] = False + image = pipe(**inputs)[0] + original_image_slice = image[0, -3:, -3:, -1] + + pipe.fuse_qkv_projections() + for _, component in pipe.components.items(): + if ( + isinstance(component, nn.Module) + and hasattr(component, "original_attn_processors") + and component.original_attn_processors is not None + ): + assert check_qkv_fusion_processors_exist(component), ( + "Something wrong with the fused attention processors. Expected all the attention processors to be fused." + ) + assert check_qkv_fusion_matches_attn_procs_length(component, component.original_attn_processors), ( + "Something wrong with the attention processors concerning the fused QKV projections." + ) + + inputs = self.get_dummy_inputs(device) + inputs["return_dict"] = False + image_fused = pipe(**inputs)[0] + image_slice_fused = image_fused[0, -3:, -3:, -1] + + pipe.unfuse_qkv_projections() + inputs = self.get_dummy_inputs(device) + inputs["return_dict"] = False + image_disabled = pipe(**inputs)[0] + image_slice_disabled = image_disabled[0, -3:, -3:, -1] + + assert np.allclose(original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2), ( + "Fusion of QKV projections shouldn't affect the outputs." + ) + assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2), ( + "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled." + ) + assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), ( + "Original outputs should match when fused QKV projections are disabled." + ) + + +class IPAdapterTesterMixin: + """ + This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. + It provides a set of common tests for pipelines that support IP Adapters. + """ + + def test_pipeline_signature(self): + parameters = inspect.signature(self.pipeline_class.__call__).parameters + + assert issubclass(self.pipeline_class, IPAdapterMixin) + self.assertIn( + "ip_adapter_image", + parameters, + "`ip_adapter_image` argument must be supported by the `__call__` method", + ) + self.assertIn( + "ip_adapter_image_embeds", + parameters, + "`ip_adapter_image_embeds` argument must be supported by the `__call__` method", + ) + + def _get_dummy_image_embeds(self, cross_attention_dim: int = 32): + return torch.randn((2, 1, cross_attention_dim), device=torch_device) + + def _get_dummy_faceid_image_embeds(self, cross_attention_dim: int = 32): + return torch.randn((2, 1, 1, cross_attention_dim), device=torch_device) + + def _get_dummy_masks(self, input_size: int = 64): + _masks = torch.zeros((1, 1, input_size, input_size), device=torch_device) + _masks[0, :, :, : int(input_size / 2)] = 1 + return _masks + + def _modify_inputs_for_ip_adapter_test(self, inputs: Dict[str, Any]): + parameters = inspect.signature(self.pipeline_class.__call__).parameters + if "image" in parameters.keys() and "strength" in parameters.keys(): + inputs["num_inference_steps"] = 4 + + inputs["output_type"] = "np" + inputs["return_dict"] = False + return inputs + + def test_ip_adapter(self, expected_max_diff: float = 1e-4, expected_pipe_slice=None): + r"""Tests for IP-Adapter. + + The following scenarios are tested: + - Single IP-Adapter with scale=0 should produce same output as no IP-Adapter. + - Multi IP-Adapter with scale=0 should produce same output as no IP-Adapter. + - Single IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter. + - Multi IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter. + """ + # Raising the tolerance for this test when it's run on a CPU because we + # compare against static slices and that can be shaky (with a VVVV low probability). + expected_max_diff = 9e-4 if torch_device == "cpu" else expected_max_diff + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components).to(torch_device) + pipe.set_progress_bar_config(disable=None) + cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32) + + # forward pass without ip adapter + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + if expected_pipe_slice is None: + output_without_adapter = pipe(**inputs)[0] + else: + output_without_adapter = expected_pipe_slice + + # 1. Single IP-Adapter test cases + adapter_state_dict = create_ip_adapter_state_dict(pipe.unet) + pipe.unet._load_ip_adapter_weights(adapter_state_dict) + + # forward pass with single ip adapter, but scale=0 which should have no effect + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] + pipe.set_ip_adapter_scale(0.0) + output_without_adapter_scale = pipe(**inputs)[0] + if expected_pipe_slice is not None: + output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten() + + # forward pass with single ip adapter, but with scale of adapter weights + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] + pipe.set_ip_adapter_scale(42.0) + output_with_adapter_scale = pipe(**inputs)[0] + if expected_pipe_slice is not None: + output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten() + + max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max() + max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max() + + self.assertLess( + max_diff_without_adapter_scale, + expected_max_diff, + "Output without ip-adapter must be same as normal inference", + ) + self.assertGreater( + max_diff_with_adapter_scale, 1e-2, "Output with ip-adapter must be different from normal inference" + ) + + # 2. Multi IP-Adapter test cases + adapter_state_dict_1 = create_ip_adapter_state_dict(pipe.unet) + adapter_state_dict_2 = create_ip_adapter_state_dict(pipe.unet) + pipe.unet._load_ip_adapter_weights([adapter_state_dict_1, adapter_state_dict_2]) + + # forward pass with multi ip adapter, but scale=0 which should have no effect + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2 + pipe.set_ip_adapter_scale([0.0, 0.0]) + output_without_multi_adapter_scale = pipe(**inputs)[0] + if expected_pipe_slice is not None: + output_without_multi_adapter_scale = output_without_multi_adapter_scale[0, -3:, -3:, -1].flatten() + + # forward pass with multi ip adapter, but with scale of adapter weights + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2 + pipe.set_ip_adapter_scale([42.0, 42.0]) + output_with_multi_adapter_scale = pipe(**inputs)[0] + if expected_pipe_slice is not None: + output_with_multi_adapter_scale = output_with_multi_adapter_scale[0, -3:, -3:, -1].flatten() + + max_diff_without_multi_adapter_scale = np.abs( + output_without_multi_adapter_scale - output_without_adapter + ).max() + max_diff_with_multi_adapter_scale = np.abs(output_with_multi_adapter_scale - output_without_adapter).max() + self.assertLess( + max_diff_without_multi_adapter_scale, + expected_max_diff, + "Output without multi-ip-adapter must be same as normal inference", + ) + self.assertGreater( + max_diff_with_multi_adapter_scale, + 1e-2, + "Output with multi-ip-adapter scale must be different from normal inference", + ) + + def test_ip_adapter_cfg(self, expected_max_diff: float = 1e-4): + parameters = inspect.signature(self.pipeline_class.__call__).parameters + + if "guidance_scale" not in parameters: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components).to(torch_device) + pipe.set_progress_bar_config(disable=None) + cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32) + + adapter_state_dict = create_ip_adapter_state_dict(pipe.unet) + pipe.unet._load_ip_adapter_weights(adapter_state_dict) + pipe.set_ip_adapter_scale(1.0) + + # forward pass with CFG not applied + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)[0].unsqueeze(0)] + inputs["guidance_scale"] = 1.0 + out_no_cfg = pipe(**inputs)[0] + + # forward pass with CFG applied + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] + inputs["guidance_scale"] = 7.5 + out_cfg = pipe(**inputs)[0] + + assert out_cfg.shape == out_no_cfg.shape + + def test_ip_adapter_masks(self, expected_max_diff: float = 1e-4): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components).to(torch_device) + pipe.set_progress_bar_config(disable=None) + cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32) + sample_size = pipe.unet.config.get("sample_size", 32) + block_out_channels = pipe.vae.config.get("block_out_channels", [128, 256, 512, 512]) + input_size = sample_size * (2 ** (len(block_out_channels) - 1)) + + # forward pass without ip adapter + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + output_without_adapter = pipe(**inputs)[0] + output_without_adapter = output_without_adapter[0, -3:, -3:, -1].flatten() + + adapter_state_dict = create_ip_adapter_state_dict(pipe.unet) + pipe.unet._load_ip_adapter_weights(adapter_state_dict) + + # forward pass with single ip adapter and masks, but scale=0 which should have no effect + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] + inputs["cross_attention_kwargs"] = {"ip_adapter_masks": [self._get_dummy_masks(input_size)]} + pipe.set_ip_adapter_scale(0.0) + output_without_adapter_scale = pipe(**inputs)[0] + output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten() + + # forward pass with single ip adapter and masks, but with scale of adapter weights + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] + inputs["cross_attention_kwargs"] = {"ip_adapter_masks": [self._get_dummy_masks(input_size)]} + pipe.set_ip_adapter_scale(42.0) + output_with_adapter_scale = pipe(**inputs)[0] + output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten() + + max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max() + max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max() + + self.assertLess( + max_diff_without_adapter_scale, + expected_max_diff, + "Output without ip-adapter must be same as normal inference", + ) + self.assertGreater( + max_diff_with_adapter_scale, 1e-3, "Output with ip-adapter must be different from normal inference" + ) + + def test_ip_adapter_faceid(self, expected_max_diff: float = 1e-4): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components).to(torch_device) + pipe.set_progress_bar_config(disable=None) + cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32) + + # forward pass without ip adapter + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + output_without_adapter = pipe(**inputs)[0] + output_without_adapter = output_without_adapter[0, -3:, -3:, -1].flatten() + + adapter_state_dict = create_ip_adapter_faceid_state_dict(pipe.unet) + pipe.unet._load_ip_adapter_weights(adapter_state_dict) + + # forward pass with single ip adapter, but scale=0 which should have no effect + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_faceid_image_embeds(cross_attention_dim)] + pipe.set_ip_adapter_scale(0.0) + output_without_adapter_scale = pipe(**inputs)[0] + output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten() + + # forward pass with single ip adapter, but with scale of adapter weights + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_faceid_image_embeds(cross_attention_dim)] + pipe.set_ip_adapter_scale(42.0) + output_with_adapter_scale = pipe(**inputs)[0] + output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten() + + max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max() + max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max() + + self.assertLess( + max_diff_without_adapter_scale, + expected_max_diff, + "Output without ip-adapter must be same as normal inference", + ) + self.assertGreater( + max_diff_with_adapter_scale, 1e-3, "Output with ip-adapter must be different from normal inference" + ) + + +class FluxIPAdapterTesterMixin: + """ + This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. + It provides a set of common tests for pipelines that support IP Adapters. + """ + + def test_pipeline_signature(self): + parameters = inspect.signature(self.pipeline_class.__call__).parameters + + assert issubclass(self.pipeline_class, FluxIPAdapterMixin) + self.assertIn( + "ip_adapter_image", + parameters, + "`ip_adapter_image` argument must be supported by the `__call__` method", + ) + self.assertIn( + "ip_adapter_image_embeds", + parameters, + "`ip_adapter_image_embeds` argument must be supported by the `__call__` method", + ) + + def _get_dummy_image_embeds(self, image_embed_dim: int = 768): + return torch.randn((1, 1, image_embed_dim), device=torch_device) + + def _modify_inputs_for_ip_adapter_test(self, inputs: Dict[str, Any]): + inputs["negative_prompt"] = "" + if "true_cfg_scale" in inspect.signature(self.pipeline_class.__call__).parameters: + inputs["true_cfg_scale"] = 4.0 + inputs["output_type"] = "np" + inputs["return_dict"] = False + return inputs + + def test_ip_adapter(self, expected_max_diff: float = 1e-4, expected_pipe_slice=None): + r"""Tests for IP-Adapter. + + The following scenarios are tested: + - Single IP-Adapter with scale=0 should produce same output as no IP-Adapter. + - Multi IP-Adapter with scale=0 should produce same output as no IP-Adapter. + - Single IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter. + - Multi IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter. + """ + # Raising the tolerance for this test when it's run on a CPU because we + # compare against static slices and that can be shaky (with a VVVV low probability). + expected_max_diff = 9e-4 if torch_device == "cpu" else expected_max_diff + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components).to(torch_device) + pipe.set_progress_bar_config(disable=None) + image_embed_dim = ( + pipe.transformer.config.pooled_projection_dim + if hasattr(pipe.transformer.config, "pooled_projection_dim") + else 768 + ) + + # forward pass without ip adapter + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + if expected_pipe_slice is None: + output_without_adapter = pipe(**inputs)[0] + else: + output_without_adapter = expected_pipe_slice + + # 1. Single IP-Adapter test cases + adapter_state_dict = create_flux_ip_adapter_state_dict(pipe.transformer) + pipe.transformer._load_ip_adapter_weights(adapter_state_dict) + + # forward pass with single ip adapter, but scale=0 which should have no effect + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] + inputs["negative_ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] + pipe.set_ip_adapter_scale(0.0) + output_without_adapter_scale = pipe(**inputs)[0] + if expected_pipe_slice is not None: + output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten() + + # forward pass with single ip adapter, but with scale of adapter weights + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] + inputs["negative_ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] + pipe.set_ip_adapter_scale(42.0) + output_with_adapter_scale = pipe(**inputs)[0] + if expected_pipe_slice is not None: + output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten() + + max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max() + max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max() + + self.assertLess( + max_diff_without_adapter_scale, + expected_max_diff, + "Output without ip-adapter must be same as normal inference", + ) + self.assertGreater( + max_diff_with_adapter_scale, 1e-2, "Output with ip-adapter must be different from normal inference" + ) + + # 2. Multi IP-Adapter test cases + adapter_state_dict_1 = create_flux_ip_adapter_state_dict(pipe.transformer) + adapter_state_dict_2 = create_flux_ip_adapter_state_dict(pipe.transformer) + pipe.transformer._load_ip_adapter_weights([adapter_state_dict_1, adapter_state_dict_2]) + + # forward pass with multi ip adapter, but scale=0 which should have no effect + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] * 2 + inputs["negative_ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] * 2 + pipe.set_ip_adapter_scale([0.0, 0.0]) + output_without_multi_adapter_scale = pipe(**inputs)[0] + if expected_pipe_slice is not None: + output_without_multi_adapter_scale = output_without_multi_adapter_scale[0, -3:, -3:, -1].flatten() + + # forward pass with multi ip adapter, but with scale of adapter weights + inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device)) + inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] * 2 + inputs["negative_ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(image_embed_dim)] * 2 + pipe.set_ip_adapter_scale([42.0, 42.0]) + output_with_multi_adapter_scale = pipe(**inputs)[0] + if expected_pipe_slice is not None: + output_with_multi_adapter_scale = output_with_multi_adapter_scale[0, -3:, -3:, -1].flatten() + + max_diff_without_multi_adapter_scale = np.abs( + output_without_multi_adapter_scale - output_without_adapter + ).max() + max_diff_with_multi_adapter_scale = np.abs(output_with_multi_adapter_scale - output_without_adapter).max() + self.assertLess( + max_diff_without_multi_adapter_scale, + expected_max_diff, + "Output without multi-ip-adapter must be same as normal inference", + ) + self.assertGreater( + max_diff_with_multi_adapter_scale, + 1e-2, + "Output with multi-ip-adapter scale must be different from normal inference", + ) + + +class PipelineLatentTesterMixin: + """ + This mixin is designed to be used with PipelineTesterMixin and unittest.TestCase classes. + It provides a set of common tests for PyTorch pipeline that has vae, e.g. + equivalence of different input and output types, etc. + """ + + @property + def image_params(self) -> frozenset: + raise NotImplementedError( + "You need to set the attribute `image_params` in the child test class. " + "`image_params` are tested for if all accepted input image types (i.e. `pt`,`pil`,`np`) are producing same results" + ) + + @property + def image_latents_params(self) -> frozenset: + raise NotImplementedError( + "You need to set the attribute `image_latents_params` in the child test class. " + "`image_latents_params` are tested for if passing latents directly are producing same results" + ) + + def get_dummy_inputs_by_type(self, device, seed=0, input_image_type="pt", output_type="np"): + inputs = self.get_dummy_inputs(device, seed) + + def convert_to_pt(image): + if isinstance(image, torch.Tensor): + input_image = image + elif isinstance(image, np.ndarray): + input_image = VaeImageProcessor.numpy_to_pt(image) + elif isinstance(image, PIL.Image.Image): + input_image = VaeImageProcessor.pil_to_numpy(image) + input_image = VaeImageProcessor.numpy_to_pt(input_image) + else: + raise ValueError(f"unsupported input_image_type {type(image)}") + return input_image + + def convert_pt_to_type(image, input_image_type): + if input_image_type == "pt": + input_image = image + elif input_image_type == "np": + input_image = VaeImageProcessor.pt_to_numpy(image) + elif input_image_type == "pil": + input_image = VaeImageProcessor.pt_to_numpy(image) + input_image = VaeImageProcessor.numpy_to_pil(input_image) + else: + raise ValueError(f"unsupported input_image_type {input_image_type}.") + return input_image + + for image_param in self.image_params: + if image_param in inputs.keys(): + inputs[image_param] = convert_pt_to_type( + convert_to_pt(inputs[image_param]).to(device), input_image_type + ) + + inputs["output_type"] = output_type + + return inputs + + def test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4): + self._test_pt_np_pil_outputs_equivalent(expected_max_diff=expected_max_diff) + + def _test_pt_np_pil_outputs_equivalent(self, expected_max_diff=1e-4, input_image_type="pt"): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + output_pt = pipe( + **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pt") + )[0] + output_np = pipe( + **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="np") + )[0] + output_pil = pipe( + **self.get_dummy_inputs_by_type(torch_device, input_image_type=input_image_type, output_type="pil") + )[0] + + max_diff = np.abs(output_pt.cpu().numpy().transpose(0, 2, 3, 1) - output_np).max() + self.assertLess( + max_diff, expected_max_diff, "`output_type=='pt'` generate different results from `output_type=='np'`" + ) + + max_diff = np.abs(np.array(output_pil[0]) - (output_np * 255).round()).max() + self.assertLess(max_diff, 2.0, "`output_type=='pil'` generate different results from `output_type=='np'`") + + def test_pt_np_pil_inputs_equivalent(self): + if len(self.image_params) == 0: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + out_input_pt = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0] + out_input_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0] + out_input_pil = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pil"))[0] + + max_diff = np.abs(out_input_pt - out_input_np).max() + self.assertLess(max_diff, 1e-4, "`input_type=='pt'` generate different result from `input_type=='np'`") + max_diff = np.abs(out_input_pil - out_input_np).max() + self.assertLess(max_diff, 1e-2, "`input_type=='pt'` generate different result from `input_type=='np'`") + + def test_latents_input(self): + if len(self.image_latents_params) == 0: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0] + + vae = components["vae"] + inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt") + generator = inputs["generator"] + for image_param in self.image_latents_params: + if image_param in inputs.keys(): + inputs[image_param] = ( + vae.encode(inputs[image_param]).latent_dist.sample(generator) * vae.config.scaling_factor + ) + out_latents_inputs = pipe(**inputs)[0] + + max_diff = np.abs(out - out_latents_inputs).max() + self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image") + + def test_multi_vae(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + block_out_channels = pipe.vae.config.block_out_channels + norm_num_groups = pipe.vae.config.norm_num_groups + + vae_classes = [AutoencoderKL, AsymmetricAutoencoderKL, ConsistencyDecoderVAE, AutoencoderTiny] + configs = [ + get_autoencoder_kl_config(block_out_channels, norm_num_groups), + get_asym_autoencoder_kl_config(block_out_channels, norm_num_groups), + get_consistency_vae_config(block_out_channels, norm_num_groups), + get_autoencoder_tiny_config(block_out_channels), + ] + + out_np = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0] + + for vae_cls, config in zip(vae_classes, configs): + vae = vae_cls(**config) + vae = vae.to(torch_device) + components["vae"] = vae + vae_pipe = self.pipeline_class(**components) + out_vae_np = vae_pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="np"))[0] + + assert out_vae_np.shape == out_np.shape + + +@require_torch +class PipelineFromPipeTesterMixin: + @property + def original_pipeline_class(self): + if "xl" in self.pipeline_class.__name__.lower(): + original_pipeline_class = StableDiffusionXLPipeline + elif "kolors" in self.pipeline_class.__name__.lower(): + original_pipeline_class = KolorsPipeline + else: + original_pipeline_class = StableDiffusionPipeline + + return original_pipeline_class + + def get_dummy_inputs_pipe(self, device, seed=0): + inputs = self.get_dummy_inputs(device, seed=seed) + inputs["output_type"] = "np" + inputs["return_dict"] = False + return inputs + + def get_dummy_inputs_for_pipe_original(self, device, seed=0): + inputs = {} + for k, v in self.get_dummy_inputs_pipe(device, seed=seed).items(): + if k in set(inspect.signature(self.original_pipeline_class.__call__).parameters.keys()): + inputs[k] = v + return inputs + + def test_from_pipe_consistent_config(self): + if self.original_pipeline_class == StableDiffusionPipeline: + original_repo = "hf-internal-testing/tiny-stable-diffusion-pipe" + original_kwargs = {"requires_safety_checker": False} + elif self.original_pipeline_class == StableDiffusionXLPipeline: + original_repo = "hf-internal-testing/tiny-stable-diffusion-xl-pipe" + original_kwargs = {"requires_aesthetics_score": True, "force_zeros_for_empty_prompt": False} + elif self.original_pipeline_class == KolorsPipeline: + original_repo = "hf-internal-testing/tiny-kolors-pipe" + original_kwargs = {"force_zeros_for_empty_prompt": False} + else: + raise ValueError( + "original_pipeline_class must be either StableDiffusionPipeline or StableDiffusionXLPipeline" + ) + + # create original_pipeline_class(sd/sdxl) + pipe_original = self.original_pipeline_class.from_pretrained(original_repo, **original_kwargs) + + # original_pipeline_class(sd/sdxl) -> pipeline_class + pipe_components = self.get_dummy_components() + pipe_additional_components = {} + for name, component in pipe_components.items(): + if name not in pipe_original.components: + pipe_additional_components[name] = component + + pipe = self.pipeline_class.from_pipe(pipe_original, **pipe_additional_components) + + # pipeline_class -> original_pipeline_class(sd/sdxl) + original_pipe_additional_components = {} + for name, component in pipe_original.components.items(): + if name not in pipe.components or not isinstance(component, pipe.components[name].__class__): + original_pipe_additional_components[name] = component + + pipe_original_2 = self.original_pipeline_class.from_pipe(pipe, **original_pipe_additional_components) + + # compare the config + original_config = {k: v for k, v in pipe_original.config.items() if not k.startswith("_")} + original_config_2 = {k: v for k, v in pipe_original_2.config.items() if not k.startswith("_")} + assert original_config_2 == original_config + + def test_from_pipe_consistent_forward_pass(self, expected_max_diff=1e-3): + components = self.get_dummy_components() + original_expected_modules, _ = self.original_pipeline_class._get_signature_keys(self.original_pipeline_class) + + # pipeline components that are also expected to be in the original pipeline + original_pipe_components = {} + # additional components that are not in the pipeline, but expected in the original pipeline + original_pipe_additional_components = {} + # additional components that are in the pipeline, but not expected in the original pipeline + current_pipe_additional_components = {} + + for name, component in components.items(): + if name in original_expected_modules: + original_pipe_components[name] = component + else: + current_pipe_additional_components[name] = component + for name in original_expected_modules: + if name not in original_pipe_components: + if name in self.original_pipeline_class._optional_components: + original_pipe_additional_components[name] = None + else: + raise ValueError(f"missing required module for {self.original_pipeline_class.__class__}: {name}") + + pipe_original = self.original_pipeline_class(**original_pipe_components, **original_pipe_additional_components) + for component in pipe_original.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_original.to(torch_device) + pipe_original.set_progress_bar_config(disable=None) + inputs = self.get_dummy_inputs_for_pipe_original(torch_device) + output_original = pipe_original(**inputs)[0] + + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + inputs = self.get_dummy_inputs_pipe(torch_device) + output = pipe(**inputs)[0] + + pipe_from_original = self.pipeline_class.from_pipe(pipe_original, **current_pipe_additional_components) + pipe_from_original.to(torch_device) + pipe_from_original.set_progress_bar_config(disable=None) + inputs = self.get_dummy_inputs_pipe(torch_device) + output_from_original = pipe_from_original(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_from_original)).max() + self.assertLess( + max_diff, + expected_max_diff, + "The outputs of the pipelines created with `from_pipe` and `__init__` are different.", + ) + + inputs = self.get_dummy_inputs_for_pipe_original(torch_device) + output_original_2 = pipe_original(**inputs)[0] + + max_diff = np.abs(to_np(output_original) - to_np(output_original_2)).max() + self.assertLess(max_diff, expected_max_diff, "`from_pipe` should not change the output of original pipeline.") + + for component in pipe_original.components.values(): + if hasattr(component, "attn_processors"): + assert all(type(proc) == AttnProcessor for proc in component.attn_processors.values()), ( + "`from_pipe` changed the attention processor in original pipeline." + ) + + @require_accelerator + @require_accelerate_version_greater("0.14.0") + def test_from_pipe_consistent_forward_pass_cpu_offload(self, expected_max_diff=1e-3): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.enable_model_cpu_offload(device=torch_device) + pipe.set_progress_bar_config(disable=None) + inputs = self.get_dummy_inputs_pipe(torch_device) + output = pipe(**inputs)[0] + + original_expected_modules, _ = self.original_pipeline_class._get_signature_keys(self.original_pipeline_class) + # pipeline components that are also expected to be in the original pipeline + original_pipe_components = {} + # additional components that are not in the pipeline, but expected in the original pipeline + original_pipe_additional_components = {} + # additional components that are in the pipeline, but not expected in the original pipeline + current_pipe_additional_components = {} + for name, component in components.items(): + if name in original_expected_modules: + original_pipe_components[name] = component + else: + current_pipe_additional_components[name] = component + for name in original_expected_modules: + if name not in original_pipe_components: + if name in self.original_pipeline_class._optional_components: + original_pipe_additional_components[name] = None + else: + raise ValueError(f"missing required module for {self.original_pipeline_class.__class__}: {name}") + + pipe_original = self.original_pipeline_class(**original_pipe_components, **original_pipe_additional_components) + for component in pipe_original.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_original.set_progress_bar_config(disable=None) + + pipe_from_original = self.pipeline_class.from_pipe(pipe_original, **current_pipe_additional_components) + for component in pipe_from_original.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe_from_original.enable_model_cpu_offload(device=torch_device) + pipe_from_original.set_progress_bar_config(disable=None) + inputs = self.get_dummy_inputs_pipe(torch_device) + output_from_original = pipe_from_original(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_from_original)).max() + self.assertLess( + max_diff, + expected_max_diff, + "The outputs of the pipelines created with `from_pipe` and `__init__` are different.", + ) + + +@require_torch +class PipelineKarrasSchedulerTesterMixin: + """ + This mixin is designed to be used with unittest.TestCase classes. + It provides a set of common tests for each PyTorch pipeline that makes use of KarrasDiffusionSchedulers + equivalence of dict and tuple outputs, etc. + """ + + def test_karras_schedulers_shape( + self, num_inference_steps_for_strength=4, num_inference_steps_for_strength_for_iterations=5 + ): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + + # make sure that PNDM does not need warm-up + pipe.scheduler.register_to_config(skip_prk_steps=True) + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + inputs = self.get_dummy_inputs(torch_device) + inputs["num_inference_steps"] = 2 + + if "strength" in inputs: + inputs["num_inference_steps"] = num_inference_steps_for_strength + inputs["strength"] = 0.5 + + outputs = [] + for scheduler_enum in KarrasDiffusionSchedulers: + if "KDPM2" in scheduler_enum.name: + inputs["num_inference_steps"] = num_inference_steps_for_strength_for_iterations + + scheduler_cls = getattr(diffusers, scheduler_enum.name) + pipe.scheduler = scheduler_cls.from_config(pipe.scheduler.config) + output = pipe(**inputs)[0] + outputs.append(output) + + if "KDPM2" in scheduler_enum.name: + inputs["num_inference_steps"] = 2 + + assert check_same_shape(outputs) + + +@require_torch +class PipelineTesterMixin: + """ + This mixin is designed to be used with unittest.TestCase classes. + It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline, + equivalence of dict and tuple outputs, etc. + """ + + # Canonical parameters that are passed to `__call__` regardless + # of the type of pipeline. They are always optional and have common + # sense default values. + required_optional_params = frozenset( + [ + "num_inference_steps", + "num_images_per_prompt", + "generator", + "latents", + "output_type", + "return_dict", + ] + ) + + # set these parameters to False in the child class if the pipeline does not support the corresponding functionality + test_attention_slicing = True + + test_xformers_attention = True + test_layerwise_casting = False + test_group_offloading = False + supports_dduf = True + + def get_generator(self, seed): + device = torch_device if torch_device != "mps" else "cpu" + generator = torch.Generator(device).manual_seed(seed) + return generator + + @property + def pipeline_class(self) -> Callable | DiffusionPipeline: + raise NotImplementedError( + "You need to set the attribute `pipeline_class = ClassNameOfPipeline` in the child test class. " + "See existing pipeline tests for reference." + ) + + def get_dummy_components(self): + raise NotImplementedError( + "You need to implement `get_dummy_components(self)` in the child test class. " + "See existing pipeline tests for reference." + ) + + def get_dummy_inputs(self, device, seed=0): + raise NotImplementedError( + "You need to implement `get_dummy_inputs(self, device, seed)` in the child test class. " + "See existing pipeline tests for reference." + ) + + @property + def params(self) -> frozenset: + raise NotImplementedError( + "You need to set the attribute `params` in the child test class. " + "`params` are checked for if all values are present in `__call__`'s signature." + " You can set `params` using one of the common set of parameters defined in `pipeline_params.py`" + " e.g., `TEXT_TO_IMAGE_PARAMS` defines the common parameters used in text to " + "image pipelines, including prompts and prompt embedding overrides." + "If your pipeline's set of arguments has minor changes from one of the common sets of arguments, " + "do not make modifications to the existing common sets of arguments. I.e. a text to image pipeline " + "with non-configurable height and width arguments should set the attribute as " + "`params = TEXT_TO_IMAGE_PARAMS - {'height', 'width'}`. " + "See existing pipeline tests for reference." + ) + + @property + def batch_params(self) -> frozenset: + raise NotImplementedError( + "You need to set the attribute `batch_params` in the child test class. " + "`batch_params` are the parameters required to be batched when passed to the pipeline's " + "`__call__` method. `pipeline_params.py` provides some common sets of parameters such as " + "`TEXT_TO_IMAGE_BATCH_PARAMS`, `IMAGE_VARIATION_BATCH_PARAMS`, etc... If your pipeline's " + "set of batch arguments has minor changes from one of the common sets of batch arguments, " + "do not make modifications to the existing common sets of batch arguments. I.e. a text to " + "image pipeline `negative_prompt` is not batched should set the attribute as " + "`batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - {'negative_prompt'}`. " + "See existing pipeline tests for reference." + ) + + @property + def callback_cfg_params(self) -> frozenset: + raise NotImplementedError( + "You need to set the attribute `callback_cfg_params` in the child test class that requires to run test_callback_cfg. " + "`callback_cfg_params` are the parameters that needs to be passed to the pipeline's callback " + "function when dynamically adjusting `guidance_scale`. They are variables that require special" + "treatment when `do_classifier_free_guidance` is `True`. `pipeline_params.py` provides some common" + " sets of parameters such as `TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS`. If your pipeline's " + "set of cfg arguments has minor changes from one of the common sets of cfg arguments, " + "do not make modifications to the existing common sets of cfg arguments. I.e. for inpaint pipeline, you " + " need to adjust batch size of `mask` and `masked_image_latents` so should set the attribute as" + "`callback_cfg_params = TEXT_TO_IMAGE_CFG_PARAMS.union({'mask', 'masked_image_latents'})`" + ) + + def setUp(self): + # clean up the VRAM before each test + super().setUp() + torch.compiler.reset() + gc.collect() + backend_empty_cache(torch_device) + + # Skip tests for pipelines that inherit from DeprecatedPipelineMixin + from diffusers.pipelines.pipeline_utils import DeprecatedPipelineMixin + + if hasattr(self, "pipeline_class") and issubclass(self.pipeline_class, DeprecatedPipelineMixin): + import pytest + + pytest.skip(reason=f"Deprecated Pipeline: {self.pipeline_class.__name__}") + + def tearDown(self): + # clean up the VRAM after each test in case of CUDA runtime errors + super().tearDown() + torch.compiler.reset() + gc.collect() + backend_empty_cache(torch_device) + + def test_save_load_local(self, expected_max_difference=5e-4): + components = self.get_dummy_components() + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0] + + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + logger.setLevel(diffusers.logging.INFO) + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + + with CaptureLogger(logger) as cap_logger: + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + for name in pipe_loaded.components.keys(): + if name not in pipe_loaded._optional_components: + assert name in str(cap_logger) + + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, expected_max_difference) + + def test_pipeline_call_signature(self): + self.assertTrue( + hasattr(self.pipeline_class, "__call__"), f"{self.pipeline_class} should have a `__call__` method" + ) + + parameters = inspect.signature(self.pipeline_class.__call__).parameters + + optional_parameters = set() + + for k, v in parameters.items(): + if v.default != inspect._empty: + optional_parameters.add(k) + + parameters = set(parameters.keys()) + parameters.remove("self") + parameters.discard("kwargs") # kwargs can be added if arguments of pipeline call function are deprecated + + remaining_required_parameters = set() + + for param in self.params: + if param not in parameters: + remaining_required_parameters.add(param) + + self.assertTrue( + len(remaining_required_parameters) == 0, + f"Required parameters not present: {remaining_required_parameters}", + ) + + remaining_required_optional_parameters = set() + + for param in self.required_optional_params: + if param not in optional_parameters: + remaining_required_optional_parameters.add(param) + + self.assertTrue( + len(remaining_required_optional_parameters) == 0, + f"Required optional parameters not present: {remaining_required_optional_parameters}", + ) + + def test_inference_batch_consistent(self, batch_sizes=[2]): + self._test_inference_batch_consistent(batch_sizes=batch_sizes) + + def _test_inference_batch_consistent( + self, batch_sizes=[2], additional_params_copy_to_batched_inputs=["num_inference_steps"], batch_generator=True + ): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + inputs["generator"] = self.get_generator(0) + + logger = logging.get_logger(pipe.__module__) + logger.setLevel(level=diffusers.logging.FATAL) + + # prepare batched inputs + batched_inputs = [] + for batch_size in batch_sizes: + batched_input = {} + batched_input.update(inputs) + + for name in self.batch_params: + if name not in inputs: + continue + + value = inputs[name] + if name == "prompt": + len_prompt = len(value) + # make unequal batch sizes + batched_input[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] + + # make last batch super long + batched_input[name][-1] = 100 * "very long" + + else: + batched_input[name] = batch_size * [value] + + if batch_generator and "generator" in inputs: + batched_input["generator"] = [self.get_generator(i) for i in range(batch_size)] + + if "batch_size" in inputs: + batched_input["batch_size"] = batch_size + + batched_inputs.append(batched_input) + + logger.setLevel(level=diffusers.logging.WARNING) + for batch_size, batched_input in zip(batch_sizes, batched_inputs): + output = pipe(**batched_input) + assert len(output[0]) == batch_size + + def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-4): + self._test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff) + + def _test_inference_batch_single_identical( + self, + batch_size=2, + expected_max_diff=1e-4, + additional_params_copy_to_batched_inputs=["num_inference_steps"], + ): + components = self.get_dummy_components() + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + for components in pipe.components.values(): + if hasattr(components, "set_default_attn_processor"): + components.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + inputs = self.get_dummy_inputs(torch_device) + # Reset generator in case it is has been used in self.get_dummy_inputs + inputs["generator"] = self.get_generator(0) + + logger = logging.get_logger(pipe.__module__) + logger.setLevel(level=diffusers.logging.FATAL) + + # batchify inputs + batched_inputs = {} + batched_inputs.update(inputs) + + for name in self.batch_params: + if name not in inputs: + continue + + value = inputs[name] + if name == "prompt": + len_prompt = len(value) + batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)] + batched_inputs[name][-1] = 100 * "very long" + + else: + batched_inputs[name] = batch_size * [value] + + if "generator" in inputs: + batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)] + + if "batch_size" in inputs: + batched_inputs["batch_size"] = batch_size + + for arg in additional_params_copy_to_batched_inputs: + batched_inputs[arg] = inputs[arg] + + output = pipe(**inputs) + output_batch = pipe(**batched_inputs) + + assert output_batch[0].shape[0] == batch_size + + max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max() + assert max_diff < expected_max_diff + + def test_dict_tuple_outputs_equivalent(self, expected_slice=None, expected_max_difference=1e-4): + components = self.get_dummy_components() + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + if expected_slice is None: + output = pipe(**self.get_dummy_inputs(generator_device))[0] + else: + output = expected_slice + + output_tuple = pipe(**self.get_dummy_inputs(generator_device), return_dict=False)[0] + + if expected_slice is None: + max_diff = np.abs(to_np(output) - to_np(output_tuple)).max() + else: + if output_tuple.ndim != 5: + max_diff = np.abs(to_np(output) - to_np(output_tuple)[0, -3:, -3:, -1].flatten()).max() + else: + max_diff = np.abs(to_np(output) - to_np(output_tuple)[0, -3:, -3:, -1, -1].flatten()).max() + + self.assertLess(max_diff, expected_max_difference) + + def test_components_function(self): + init_components = self.get_dummy_components() + init_components = {k: v for k, v in init_components.items() if not isinstance(v, (str, int, float))} + + pipe = self.pipeline_class(**init_components) + + self.assertTrue(hasattr(pipe, "components")) + self.assertTrue(set(pipe.components.keys()) == set(init_components.keys())) + + @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU") + @require_accelerator + def test_float16_inference(self, expected_max_diff=5e-2): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + components = self.get_dummy_components() + pipe_fp16 = self.pipeline_class(**components) + for component in pipe_fp16.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_fp16.to(torch_device, torch.float16) + pipe_fp16.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + # Reset generator in case it is used inside dummy inputs + if "generator" in inputs: + inputs["generator"] = self.get_generator(0) + output = pipe(**inputs)[0] + + fp16_inputs = self.get_dummy_inputs(torch_device) + # Reset generator in case it is used inside dummy inputs + if "generator" in fp16_inputs: + fp16_inputs["generator"] = self.get_generator(0) + output_fp16 = pipe_fp16(**fp16_inputs)[0] + + if isinstance(output, torch.Tensor): + output = output.cpu() + output_fp16 = output_fp16.cpu() + + max_diff = numpy_cosine_similarity_distance(output.flatten(), output_fp16.flatten()) + assert max_diff < expected_max_diff + + @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU") + @require_accelerator + def test_save_load_float16(self, expected_max_diff=1e-2): + components = self.get_dummy_components() + for name, module in components.items(): + # Account for components with _keep_in_fp32_modules + if hasattr(module, "_keep_in_fp32_modules") and module._keep_in_fp32_modules is not None: + for name, param in module.named_parameters(): + if any( + module_to_keep_in_fp32 in name.split(".") + for module_to_keep_in_fp32 in module._keep_in_fp32_modules + ): + param.data = param.data.to(torch_device).to(torch.float32) + else: + param.data = param.data.to(torch_device).to(torch.float16) + + elif hasattr(module, "half"): + components[name] = module.to(torch_device).half() + + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for name, component in pipe_loaded.components.items(): + if hasattr(component, "dtype"): + self.assertTrue( + component.dtype == torch.float16, + f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.", + ) + + inputs = self.get_dummy_inputs(torch_device) + output_loaded = pipe_loaded(**inputs)[0] + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess( + max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading." + ) + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + if not hasattr(self.pipeline_class, "_optional_components"): + return + if not self.pipeline_class._optional_components: + return + components = self.get_dummy_components() + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + # set all optional components to None + for optional_component in pipe._optional_components: + setattr(pipe, optional_component, None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for optional_component in pipe._optional_components: + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, expected_max_difference) + + @require_accelerator + def test_to_device(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.set_progress_bar_config(disable=None) + + pipe.to("cpu") + model_devices = [ + component.device.type for component in components.values() if getattr(component, "device", None) + ] + self.assertTrue(all(device == "cpu" for device in model_devices)) + + output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0] + self.assertTrue(np.isnan(output_cpu).sum() == 0) + + pipe.to(torch_device) + model_devices = [ + component.device.type for component in components.values() if getattr(component, "device", None) + ] + self.assertTrue(all(device == torch_device for device in model_devices)) + + output_device = pipe(**self.get_dummy_inputs(torch_device))[0] + self.assertTrue(np.isnan(to_np(output_device)).sum() == 0) + + def test_to_dtype(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.set_progress_bar_config(disable=None) + + model_dtypes = [component.dtype for component in components.values() if getattr(component, "dtype", None)] + self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes)) + + pipe.to(dtype=torch.float16) + model_dtypes = [component.dtype for component in components.values() if getattr(component, "dtype", None)] + self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes)) + + def test_attention_slicing_forward_pass(self, expected_max_diff=1e-3): + self._test_attention_slicing_forward_pass(expected_max_diff=expected_max_diff) + + def _test_attention_slicing_forward_pass( + self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 + ): + if not self.test_attention_slicing: + return + + components = self.get_dummy_components() + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + output_without_slicing = pipe(**inputs)[0] + + pipe.enable_attention_slicing(slice_size=1) + inputs = self.get_dummy_inputs(generator_device) + output_with_slicing1 = pipe(**inputs)[0] + + pipe.enable_attention_slicing(slice_size=2) + inputs = self.get_dummy_inputs(generator_device) + output_with_slicing2 = pipe(**inputs)[0] + + if test_max_difference: + max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max() + max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max() + self.assertLess( + max(max_diff1, max_diff2), + expected_max_diff, + "Attention slicing should not affect the inference results", + ) + + if test_mean_pixel_difference: + assert_mean_pixel_difference(to_np(output_with_slicing1[0]), to_np(output_without_slicing[0])) + assert_mean_pixel_difference(to_np(output_with_slicing2[0]), to_np(output_without_slicing[0])) + + @require_accelerator + @require_accelerate_version_greater("0.14.0") + def test_sequential_cpu_offload_forward_pass(self, expected_max_diff=1e-4): + import accelerate + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_without_offload = pipe(**inputs)[0] + + pipe.enable_sequential_cpu_offload(device=torch_device) + assert pipe._execution_device.type == torch_device + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_with_offload = pipe(**inputs)[0] + + max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() + self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results") + + # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly + offloaded_modules = { + k: v + for k, v in pipe.components.items() + if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload + } + # 1. all offloaded modules should be saved to cpu and moved to meta device + self.assertTrue( + all(v.device.type == "meta" for v in offloaded_modules.values()), + f"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'meta']}", + ) + # 2. all offloaded modules should have hook installed + self.assertTrue( + all(hasattr(v, "_hf_hook") for k, v in offloaded_modules.items()), + f"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}", + ) + # 3. all offloaded modules should have correct hooks installed, should be either one of these two + # - `AlignDevicesHook` + # - a SequentialHook` that contains `AlignDevicesHook` + offloaded_modules_with_incorrect_hooks = {} + for k, v in offloaded_modules.items(): + if hasattr(v, "_hf_hook"): + if isinstance(v._hf_hook, accelerate.hooks.SequentialHook): + # if it is a `SequentialHook`, we loop through its `hooks` attribute to check if it only contains `AlignDevicesHook` + for hook in v._hf_hook.hooks: + if not isinstance(hook, accelerate.hooks.AlignDevicesHook): + offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook.hooks[0]) + elif not isinstance(v._hf_hook, accelerate.hooks.AlignDevicesHook): + offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook) + + self.assertTrue( + len(offloaded_modules_with_incorrect_hooks) == 0, + f"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}", + ) + + @require_accelerator + @require_accelerate_version_greater("0.17.0") + def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4): + import accelerate + + generator_device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_without_offload = pipe(**inputs)[0] + + pipe.enable_model_cpu_offload(device=torch_device) + assert pipe._execution_device.type == torch_device + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_with_offload = pipe(**inputs)[0] + + max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() + self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results") + + # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly + offloaded_modules = { + k: v + for k, v in pipe.components.items() + if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload + } + # 1. check if all offloaded modules are saved to cpu + self.assertTrue( + all(v.device.type == "cpu" for v in offloaded_modules.values()), + f"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'cpu']}", + ) + # 2. check if all offloaded modules have hooks installed + self.assertTrue( + all(hasattr(v, "_hf_hook") for k, v in offloaded_modules.items()), + f"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}", + ) + # 3. check if all offloaded modules have correct type of hooks installed, should be `CpuOffload` + offloaded_modules_with_incorrect_hooks = {} + for k, v in offloaded_modules.items(): + if hasattr(v, "_hf_hook") and not isinstance(v._hf_hook, accelerate.hooks.CpuOffload): + offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook) + + self.assertTrue( + len(offloaded_modules_with_incorrect_hooks) == 0, + f"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}", + ) + + @require_accelerator + @require_accelerate_version_greater("0.17.0") + def test_cpu_offload_forward_pass_twice(self, expected_max_diff=2e-4): + import accelerate + + generator_device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.set_progress_bar_config(disable=None) + + pipe.enable_model_cpu_offload() + inputs = self.get_dummy_inputs(generator_device) + output_with_offload = pipe(**inputs)[0] + + pipe.enable_model_cpu_offload() + inputs = self.get_dummy_inputs(generator_device) + output_with_offload_twice = pipe(**inputs)[0] + + max_diff = np.abs(to_np(output_with_offload) - to_np(output_with_offload_twice)).max() + self.assertLess( + max_diff, expected_max_diff, "running CPU offloading 2nd time should not affect the inference results" + ) + + # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly + offloaded_modules = { + k: v + for k, v in pipe.components.items() + if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload + } + # 1. check if all offloaded modules are saved to cpu + self.assertTrue( + all(v.device.type == "cpu" for v in offloaded_modules.values()), + f"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'cpu']}", + ) + # 2. check if all offloaded modules have hooks installed + self.assertTrue( + all(hasattr(v, "_hf_hook") for k, v in offloaded_modules.items()), + f"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}", + ) + # 3. check if all offloaded modules have correct type of hooks installed, should be `CpuOffload` + offloaded_modules_with_incorrect_hooks = {} + for k, v in offloaded_modules.items(): + if hasattr(v, "_hf_hook") and not isinstance(v._hf_hook, accelerate.hooks.CpuOffload): + offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook) + + self.assertTrue( + len(offloaded_modules_with_incorrect_hooks) == 0, + f"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}", + ) + + @require_accelerator + @require_accelerate_version_greater("0.14.0") + def test_sequential_offload_forward_pass_twice(self, expected_max_diff=2e-4): + import accelerate + + generator_device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.set_progress_bar_config(disable=None) + + pipe.enable_sequential_cpu_offload(device=torch_device) + inputs = self.get_dummy_inputs(generator_device) + output_with_offload = pipe(**inputs)[0] + + pipe.enable_sequential_cpu_offload(device=torch_device) + inputs = self.get_dummy_inputs(generator_device) + output_with_offload_twice = pipe(**inputs)[0] + + max_diff = np.abs(to_np(output_with_offload) - to_np(output_with_offload_twice)).max() + self.assertLess( + max_diff, expected_max_diff, "running sequential offloading second time should have the inference results" + ) + + # make sure all `torch.nn.Module` components (except those in `self._exclude_from_cpu_offload`) are offloaded correctly + offloaded_modules = { + k: v + for k, v in pipe.components.items() + if isinstance(v, torch.nn.Module) and k not in pipe._exclude_from_cpu_offload + } + # 1. check if all offloaded modules are moved to meta device + self.assertTrue( + all(v.device.type == "meta" for v in offloaded_modules.values()), + f"Not offloaded: {[k for k, v in offloaded_modules.items() if v.device.type != 'meta']}", + ) + # 2. check if all offloaded modules have hook installed + self.assertTrue( + all(hasattr(v, "_hf_hook") for k, v in offloaded_modules.items()), + f"No hook attached: {[k for k, v in offloaded_modules.items() if not hasattr(v, '_hf_hook')]}", + ) + # 3. check if all offloaded modules have correct hooks installed, should be either one of these two + # - `AlignDevicesHook` + # - a SequentialHook` that contains `AlignDevicesHook` + offloaded_modules_with_incorrect_hooks = {} + for k, v in offloaded_modules.items(): + if hasattr(v, "_hf_hook"): + if isinstance(v._hf_hook, accelerate.hooks.SequentialHook): + # if it is a `SequentialHook`, we loop through its `hooks` attribute to check if it only contains `AlignDevicesHook` + for hook in v._hf_hook.hooks: + if not isinstance(hook, accelerate.hooks.AlignDevicesHook): + offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook.hooks[0]) + elif not isinstance(v._hf_hook, accelerate.hooks.AlignDevicesHook): + offloaded_modules_with_incorrect_hooks[k] = type(v._hf_hook) + + self.assertTrue( + len(offloaded_modules_with_incorrect_hooks) == 0, + f"Not installed correct hook: {offloaded_modules_with_incorrect_hooks}", + ) + + @unittest.skipIf( + torch_device != "cuda" or not is_xformers_available(), + reason="XFormers attention is only available with CUDA and `xformers` installed", + ) + def test_xformers_attention_forwardGenerator_pass(self): + self._test_xformers_attention_forwardGenerator_pass() + + def _test_xformers_attention_forwardGenerator_pass( + self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-4 + ): + if not self.test_xformers_attention: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output_without_offload = pipe(**inputs)[0] + output_without_offload = ( + output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload + ) + + pipe.enable_xformers_memory_efficient_attention() + inputs = self.get_dummy_inputs(torch_device) + output_with_offload = pipe(**inputs)[0] + output_with_offload = ( + output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload + ) + + if test_max_difference: + max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() + self.assertLess(max_diff, expected_max_diff, "XFormers attention should not affect the inference results") + + if test_mean_pixel_difference: + assert_mean_pixel_difference(output_with_offload[0], output_without_offload[0]) + + def test_num_images_per_prompt(self): + sig = inspect.signature(self.pipeline_class.__call__) + + if "num_images_per_prompt" not in sig.parameters: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + batch_sizes = [1, 2] + num_images_per_prompts = [1, 2] + + for batch_size in batch_sizes: + for num_images_per_prompt in num_images_per_prompts: + inputs = self.get_dummy_inputs(torch_device) + + for key in inputs.keys(): + if key in self.batch_params: + inputs[key] = batch_size * [inputs[key]] + + images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0] + + assert images.shape[0] == batch_size * num_images_per_prompt + + def test_cfg(self): + sig = inspect.signature(self.pipeline_class.__call__) + + if "guidance_scale" not in sig.parameters: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + + inputs["guidance_scale"] = 1.0 + out_no_cfg = pipe(**inputs)[0] + + inputs["guidance_scale"] = 7.5 + out_cfg = pipe(**inputs)[0] + + assert out_cfg.shape == out_no_cfg.shape + + def test_callback_inputs(self): + sig = inspect.signature(self.pipeline_class.__call__) + has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters + has_callback_step_end = "callback_on_step_end" in sig.parameters + + if not (has_callback_tensor_inputs and has_callback_step_end): + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + self.assertTrue( + hasattr(pipe, "_callback_tensor_inputs"), + f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs", + ) + + def callback_inputs_subset(pipe, i, t, callback_kwargs): + # iterate over callback args + for tensor_name, tensor_value in callback_kwargs.items(): + # check that we're only passing in allowed tensor inputs + assert tensor_name in pipe._callback_tensor_inputs + + return callback_kwargs + + def callback_inputs_all(pipe, i, t, callback_kwargs): + for tensor_name in pipe._callback_tensor_inputs: + assert tensor_name in callback_kwargs + + # iterate over callback args + for tensor_name, tensor_value in callback_kwargs.items(): + # check that we're only passing in allowed tensor inputs + assert tensor_name in pipe._callback_tensor_inputs + + return callback_kwargs + + inputs = self.get_dummy_inputs(torch_device) + + # Test passing in a subset + inputs["callback_on_step_end"] = callback_inputs_subset + inputs["callback_on_step_end_tensor_inputs"] = ["latents"] + inputs["output_type"] = "latent" + output = pipe(**inputs)[0] + + # Test passing in a everything + inputs["callback_on_step_end"] = callback_inputs_all + inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs + inputs["output_type"] = "latent" + output = pipe(**inputs)[0] + + def callback_inputs_change_tensor(pipe, i, t, callback_kwargs): + is_last = i == (pipe.num_timesteps - 1) + if is_last: + callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"]) + return callback_kwargs + + inputs["callback_on_step_end"] = callback_inputs_change_tensor + inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs + inputs["output_type"] = "latent" + output = pipe(**inputs)[0] + assert output.abs().sum() == 0 + + def test_callback_cfg(self): + sig = inspect.signature(self.pipeline_class.__call__) + has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters + has_callback_step_end = "callback_on_step_end" in sig.parameters + + if not (has_callback_tensor_inputs and has_callback_step_end): + return + + if "guidance_scale" not in sig.parameters: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + self.assertTrue( + hasattr(pipe, "_callback_tensor_inputs"), + f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs", + ) + + def callback_increase_guidance(pipe, i, t, callback_kwargs): + pipe._guidance_scale += 1.0 + + return callback_kwargs + + inputs = self.get_dummy_inputs(torch_device) + + # use cfg guidance because some pipelines modify the shape of the latents + # outside of the denoising loop + inputs["guidance_scale"] = 2.0 + inputs["callback_on_step_end"] = callback_increase_guidance + inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs + _ = pipe(**inputs)[0] + + # we increase the guidance scale by 1.0 at every step + # check that the guidance scale is increased by the number of scheduler timesteps + # accounts for models that modify the number of inference steps based on strength + assert pipe.guidance_scale == (inputs["guidance_scale"] + pipe.num_timesteps) + + def test_serialization_with_variants(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + model_components = [ + component_name for component_name, component in pipe.components.items() if isinstance(component, nn.Module) + ] + variant = "fp16" + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, variant=variant, safe_serialization=False) + + with open(f"{tmpdir}/model_index.json", "r") as f: + config = json.load(f) + + for subfolder in os.listdir(tmpdir): + if not os.path.isfile(subfolder) and subfolder in model_components: + folder_path = os.path.join(tmpdir, subfolder) + is_folder = os.path.isdir(folder_path) and subfolder in config + assert is_folder and any(p.split(".")[1].startswith(variant) for p in os.listdir(folder_path)) + + def test_loading_with_variants(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + variant = "fp16" + + def is_nan(tensor): + if tensor.ndimension() == 0: + has_nan = torch.isnan(tensor).item() + else: + has_nan = torch.isnan(tensor).any() + return has_nan + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, variant=variant, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, variant=variant) + + model_components_pipe = { + component_name: component + for component_name, component in pipe.components.items() + if isinstance(component, nn.Module) + } + model_components_pipe_loaded = { + component_name: component + for component_name, component in pipe_loaded.components.items() + if isinstance(component, nn.Module) + } + for component_name in model_components_pipe: + pipe_component = model_components_pipe[component_name] + pipe_loaded_component = model_components_pipe_loaded[component_name] + + model_loaded_params = dict(pipe_loaded_component.named_parameters()) + model_original_params = dict(pipe_component.named_parameters()) + + for name, p1 in model_original_params.items(): + # Skip tied weights that aren't saved with variants (transformers v5 behavior) + if name not in model_loaded_params: + continue + + p2 = model_loaded_params[name] + # nan check for luminanext (mps). + if not (is_nan(p1) and is_nan(p2)): + self.assertTrue(torch.equal(p1, p2)) + + def test_loading_with_incorrect_variants_raises_error(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + variant = "fp16" + + with tempfile.TemporaryDirectory() as tmpdir: + # Don't save with variants. + pipe.save_pretrained(tmpdir, safe_serialization=False) + + with self.assertRaises(ValueError) as error: + _ = self.pipeline_class.from_pretrained(tmpdir, variant=variant) + + assert f"You are trying to load the model files of the `variant={variant}`" in str(error.exception) + + def test_encode_prompt_works_in_isolation(self, extra_required_param_value_dict=None, atol=1e-4, rtol=1e-4): + if not hasattr(self.pipeline_class, "encode_prompt"): + return + + components = self.get_dummy_components() + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + + # We initialize the pipeline with only text encoders and tokenizers, + # mimicking a real-world scenario. + components_with_text_encoders = {} + for k in components: + if "text" in k or "tokenizer" in k: + components_with_text_encoders[k] = components[k] + else: + components_with_text_encoders[k] = None + pipe_with_just_text_encoder = self.pipeline_class(**components_with_text_encoders) + pipe_with_just_text_encoder = pipe_with_just_text_encoder.to(torch_device) + + # Get inputs and also the args of `encode_prompts`. + inputs = self.get_dummy_inputs(torch_device) + encode_prompt_signature = inspect.signature(pipe_with_just_text_encoder.encode_prompt) + encode_prompt_parameters = list(encode_prompt_signature.parameters.values()) + + # Required args in encode_prompt with those with no default. + required_params = [] + for param in encode_prompt_parameters: + if param.name == "self" or param.name == "kwargs": + continue + if param.default is inspect.Parameter.empty: + required_params.append(param.name) + + # Craft inputs for the `encode_prompt()` method to run in isolation. + encode_prompt_param_names = [p.name for p in encode_prompt_parameters if p.name != "self"] + input_keys = list(inputs.keys()) + encode_prompt_inputs = {k: inputs.pop(k) for k in input_keys if k in encode_prompt_param_names} + + pipe_call_signature = inspect.signature(pipe_with_just_text_encoder.__call__) + pipe_call_parameters = pipe_call_signature.parameters + + # For each required arg in encode_prompt, check if it's missing + # in encode_prompt_inputs. If so, see if __call__ has a default + # for that arg and use it if available. + for required_param_name in required_params: + if required_param_name not in encode_prompt_inputs: + pipe_call_param = pipe_call_parameters.get(required_param_name, None) + if pipe_call_param is not None and pipe_call_param.default is not inspect.Parameter.empty: + # Use the default from pipe.__call__ + encode_prompt_inputs[required_param_name] = pipe_call_param.default + elif extra_required_param_value_dict is not None and isinstance(extra_required_param_value_dict, dict): + encode_prompt_inputs[required_param_name] = extra_required_param_value_dict[required_param_name] + else: + raise ValueError( + f"Required parameter '{required_param_name}' in " + f"encode_prompt has no default in either encode_prompt or __call__." + ) + + # Compute `encode_prompt()`. + with torch.no_grad(): + encoded_prompt_outputs = pipe_with_just_text_encoder.encode_prompt(**encode_prompt_inputs) + + # Programmatically determine the return names of `encode_prompt.` + ast_visitor = ReturnNameVisitor() + encode_prompt_tree = ast_visitor.get_ast_tree(cls=self.pipeline_class) + ast_visitor.visit(encode_prompt_tree) + prompt_embed_kwargs = ast_visitor.return_names + prompt_embeds_kwargs = dict(zip(prompt_embed_kwargs, encoded_prompt_outputs)) + + # Pack the outputs of `encode_prompt`. + adapted_prompt_embeds_kwargs = { + k: prompt_embeds_kwargs.pop(k) for k in list(prompt_embeds_kwargs.keys()) if k in pipe_call_parameters + } + + # now initialize a pipeline without text encoders and compute outputs with the + # `encode_prompt()` outputs and other relevant inputs. + components_with_text_encoders = {} + for k in components: + if "text" in k or "tokenizer" in k: + components_with_text_encoders[k] = None + else: + components_with_text_encoders[k] = components[k] + pipe_without_text_encoders = self.pipeline_class(**components_with_text_encoders).to(torch_device) + + # Set `negative_prompt` to None as we have already calculated its embeds + # if it was present in `inputs`. This is because otherwise we will interfere wrongly + # for non-None `negative_prompt` values as defaults (PixArt for example). + pipe_without_tes_inputs = {**inputs, **adapted_prompt_embeds_kwargs} + if ( + pipe_call_parameters.get("negative_prompt", None) is not None + and pipe_call_parameters.get("negative_prompt").default is not None + ): + pipe_without_tes_inputs.update({"negative_prompt": None}) + + # Pipelines like attend and excite have `prompt` as a required argument. + if ( + pipe_call_parameters.get("prompt", None) is not None + and pipe_call_parameters.get("prompt").default is inspect.Parameter.empty + and pipe_call_parameters.get("prompt_embeds", None) is not None + and pipe_call_parameters.get("prompt_embeds").default is None + ): + pipe_without_tes_inputs.update({"prompt": None}) + + pipe_out = pipe_without_text_encoders(**pipe_without_tes_inputs)[0] + + # Compare against regular pipeline outputs. + full_pipe = self.pipeline_class(**components).to(torch_device) + inputs = self.get_dummy_inputs(torch_device) + pipe_out_2 = full_pipe(**inputs)[0] + + if isinstance(pipe_out, np.ndarray) and isinstance(pipe_out_2, np.ndarray): + self.assertTrue(np.allclose(pipe_out, pipe_out_2, atol=atol, rtol=rtol)) + elif isinstance(pipe_out, torch.Tensor) and isinstance(pipe_out_2, torch.Tensor): + self.assertTrue(torch.allclose(pipe_out, pipe_out_2, atol=atol, rtol=rtol)) + + def test_StableDiffusionMixin_component(self): + """Any pipeline that have LDMFuncMixin should have vae and unet components.""" + if not issubclass(self.pipeline_class, StableDiffusionMixin): + return + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + self.assertTrue(hasattr(pipe, "vae") and isinstance(pipe.vae, (AutoencoderKL, AutoencoderTiny))) + self.assertTrue( + hasattr(pipe, "unet") + and isinstance( + pipe.unet, + (UNet2DConditionModel, UNet3DConditionModel, I2VGenXLUNet, UNetMotionModel, UNetControlNetXSModel), + ) + ) + + @require_hf_hub_version_greater("0.26.5") + @require_transformers_version_greater("4.47.1") + def test_save_load_dduf(self, atol=1e-4, rtol=1e-4): + if not self.supports_dduf: + return + + from huggingface_hub import export_folder_as_dduf + + components = self.get_dummy_components() + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device="cpu") + inputs.pop("generator") + inputs["generator"] = torch.manual_seed(0) + + pipeline_out = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + dduf_filename = os.path.join(tmpdir, f"{pipe.__class__.__name__.lower()}.dduf") + pipe.save_pretrained(tmpdir, safe_serialization=True) + export_folder_as_dduf(dduf_filename, folder_path=tmpdir) + loaded_pipe = self.pipeline_class.from_pretrained(tmpdir, dduf_file=dduf_filename).to(torch_device) + + inputs["generator"] = torch.manual_seed(0) + loaded_pipeline_out = loaded_pipe(**inputs)[0] + + if isinstance(pipeline_out, np.ndarray) and isinstance(loaded_pipeline_out, np.ndarray): + assert np.allclose(pipeline_out, loaded_pipeline_out, atol=atol, rtol=rtol) + elif isinstance(pipeline_out, torch.Tensor) and isinstance(loaded_pipeline_out, torch.Tensor): + assert torch.allclose(pipeline_out, loaded_pipeline_out, atol=atol, rtol=rtol) + + def test_layerwise_casting_inference(self): + if not self.test_layerwise_casting: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device, dtype=torch.bfloat16) + pipe.set_progress_bar_config(disable=None) + + denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet + denoiser.enable_layerwise_casting(storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16) + + inputs = self.get_dummy_inputs(torch_device) + _ = pipe(**inputs)[0] + + @require_torch_accelerator + def test_group_offloading_inference(self): + if not self.test_group_offloading: + return + + def create_pipe(): + torch.manual_seed(0) + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.set_progress_bar_config(disable=None) + return pipe + + def enable_group_offload_on_component(pipe, group_offloading_kwargs): + # We intentionally don't test VAE's here. This is because some tests enable tiling on the VAE. If + # tiling is enabled and a forward pass is run, when accelerator streams are used, the execution order of + # the layers is not traced correctly. This causes errors. For apply group offloading to VAE, a + # warmup forward pass (even with dummy small inputs) is recommended. + for component_name in [ + "text_encoder", + "text_encoder_2", + "text_encoder_3", + "transformer", + "unet", + "controlnet", + ]: + if not hasattr(pipe, component_name): + continue + component = getattr(pipe, component_name) + if not getattr(component, "_supports_group_offloading", True): + continue + if hasattr(component, "enable_group_offload"): + # For diffusers ModelMixin implementations + component.enable_group_offload(torch.device(torch_device), **group_offloading_kwargs) + else: + # For other models not part of diffusers + apply_group_offloading( + component, onload_device=torch.device(torch_device), **group_offloading_kwargs + ) + self.assertTrue( + all( + module._diffusers_hook.get_hook("group_offloading") is not None + for module in component.modules() + if hasattr(module, "_diffusers_hook") + ) + ) + for component_name in ["vae", "vqvae", "image_encoder"]: + component = getattr(pipe, component_name, None) + if isinstance(component, torch.nn.Module): + component.to(torch_device) + + def run_forward(pipe): + torch.manual_seed(0) + inputs = self.get_dummy_inputs(torch_device) + return pipe(**inputs)[0] + + pipe = create_pipe().to(torch_device) + output_without_group_offloading = run_forward(pipe) + + pipe = create_pipe() + enable_group_offload_on_component(pipe, {"offload_type": "block_level", "num_blocks_per_group": 1}) + output_with_group_offloading1 = run_forward(pipe) + + pipe = create_pipe() + enable_group_offload_on_component(pipe, {"offload_type": "leaf_level"}) + output_with_group_offloading2 = run_forward(pipe) + + if torch.is_tensor(output_without_group_offloading): + output_without_group_offloading = output_without_group_offloading.detach().cpu().numpy() + output_with_group_offloading1 = output_with_group_offloading1.detach().cpu().numpy() + output_with_group_offloading2 = output_with_group_offloading2.detach().cpu().numpy() + + self.assertTrue(np.allclose(output_without_group_offloading, output_with_group_offloading1, atol=1e-4)) + self.assertTrue(np.allclose(output_without_group_offloading, output_with_group_offloading2, atol=1e-4)) + + def test_torch_dtype_dict(self): + components = self.get_dummy_components() + if not components: + self.skipTest("No dummy components defined.") + + pipe = self.pipeline_class(**components) + specified_key = next(iter(components.keys())) + + with tempfile.TemporaryDirectory(ignore_cleanup_errors=True) as tmpdirname: + pipe.save_pretrained(tmpdirname, safe_serialization=False) + torch_dtype_dict = {specified_key: torch.bfloat16, "default": torch.float16} + loaded_pipe = self.pipeline_class.from_pretrained(tmpdirname, torch_dtype=torch_dtype_dict) + + for name, component in loaded_pipe.components.items(): + if isinstance(component, torch.nn.Module) and hasattr(component, "dtype"): + expected_dtype = torch_dtype_dict.get(name, torch_dtype_dict.get("default", torch.float32)) + self.assertEqual( + component.dtype, + expected_dtype, + f"Component '{name}' has dtype {component.dtype} but expected {expected_dtype}", + ) + + def test_pipeline_with_accelerator_device_map(self, expected_max_difference=1e-4): + components = self.get_dummy_components() + # Set text encoders to eval mode to match from_pretrained behavior + # This ensures deterministic outputs when models are loaded with device_map + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + torch.manual_seed(0) + inputs = self.get_dummy_inputs(torch_device) + inputs["generator"] = torch.manual_seed(0) + out = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + loaded_pipe = self.pipeline_class.from_pretrained(tmpdir, device_map=torch_device) + for component in loaded_pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + inputs["generator"] = torch.manual_seed(0) + loaded_out = loaded_pipe(**inputs)[0] + max_diff = np.abs(to_np(out) - to_np(loaded_out)).max() + self.assertLess(max_diff, expected_max_difference) + + @require_torch_accelerator + def test_pipeline_level_group_offloading_sanity_checks(self): + components = self.get_dummy_components() + pipe: DiffusionPipeline = self.pipeline_class(**components) + + for name, component in pipe.components.items(): + if hasattr(component, "_supports_group_offloading"): + if not component._supports_group_offloading: + pytest.skip(f"{self.pipeline_class.__name__} is not suitable for this test.") + + module_names = sorted( + [name for name, component in pipe.components.items() if isinstance(component, torch.nn.Module)] + ) + exclude_module_name = module_names[0] + offload_device = "cpu" + pipe.enable_group_offload( + onload_device=torch_device, + offload_device=offload_device, + offload_type="leaf_level", + exclude_modules=exclude_module_name, + ) + excluded_module = getattr(pipe, exclude_module_name) + self.assertTrue(torch.device(excluded_module.device).type == torch.device(torch_device).type) + + for name, component in pipe.components.items(): + if name not in [exclude_module_name] and isinstance(component, torch.nn.Module): + # `component.device` prints the `onload_device` type. We should probably override the + # `device` property in `ModelMixin`. + # Skip modules with no parameters (e.g., dummy safety checkers with only buffers) + params = list(component.parameters()) + if not params: + continue + component_device = params[0].device + self.assertTrue(torch.device(component_device).type == torch.device(offload_device).type) + + @require_torch_accelerator + def test_pipeline_level_group_offloading_inference(self, expected_max_difference=1e-4): + components = self.get_dummy_components() + pipe: DiffusionPipeline = self.pipeline_class(**components) + + for name, component in pipe.components.items(): + if hasattr(component, "_supports_group_offloading"): + if not component._supports_group_offloading: + pytest.skip(f"{self.pipeline_class.__name__} is not suitable for this test.") + + # Regular inference. + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + torch.manual_seed(0) + inputs = self.get_dummy_inputs(torch_device) + inputs["generator"] = torch.manual_seed(0) + out = pipe(**inputs)[0] + + pipe.to("cpu") + del pipe + + # Inference with offloading + pipe: DiffusionPipeline = self.pipeline_class(**components) + offload_device = "cpu" + pipe.enable_group_offload( + onload_device=torch_device, + offload_device=offload_device, + offload_type="leaf_level", + ) + pipe.set_progress_bar_config(disable=None) + inputs["generator"] = torch.manual_seed(0) + out_offload = pipe(**inputs)[0] + + max_diff = np.abs(to_np(out) - to_np(out_offload)).max() + self.assertLess(max_diff, expected_max_difference) + + +@is_staging_test +class PipelinePushToHubTester(unittest.TestCase): + identifier = uuid.uuid4() + repo_id = f"test-pipeline-{identifier}" + org_repo_id = f"valid_org/{repo_id}-org" + + def get_pipeline_components(self): + unet = UNet2DConditionModel( + block_out_channels=(32, 64), + layers_per_block=2, + sample_size=32, + in_channels=4, + out_channels=4, + down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), + cross_attention_dim=32, + ) + + scheduler = DDIMScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + ) + + vae = AutoencoderKL( + block_out_channels=[32, 64], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + ) + + text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + ) + text_encoder = CLIPTextModel(text_encoder_config) + + with tempfile.TemporaryDirectory() as tmpdir: + dummy_vocab = {"<|startoftext|>": 0, "<|endoftext|>": 1, "!": 2} + vocab_path = os.path.join(tmpdir, "vocab.json") + with open(vocab_path, "w") as f: + json.dump(dummy_vocab, f) + + merges = "Ġ t\nĠt h" + merges_path = os.path.join(tmpdir, "merges.txt") + with open(merges_path, "w") as f: + f.writelines(merges) + tokenizer = CLIPTokenizer(vocab_file=vocab_path, merges_file=merges_path) + + components = { + "unet": unet, + "scheduler": scheduler, + "vae": vae, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "safety_checker": None, + "feature_extractor": None, + } + return components + + def test_push_to_hub(self): + components = self.get_pipeline_components() + pipeline = StableDiffusionPipeline(**components) + pipeline.push_to_hub(self.repo_id, token=TOKEN) + + new_model = UNet2DConditionModel.from_pretrained(f"{USER}/{self.repo_id}", subfolder="unet") + unet = components["unet"] + for p1, p2 in zip(unet.parameters(), new_model.parameters()): + self.assertTrue(torch.equal(p1, p2)) + + # Reset repo + delete_repo(token=TOKEN, repo_id=self.repo_id) + + # Push to hub via save_pretrained + with tempfile.TemporaryDirectory() as tmp_dir: + pipeline.save_pretrained(tmp_dir, repo_id=self.repo_id, push_to_hub=True, token=TOKEN) + + new_model = UNet2DConditionModel.from_pretrained(f"{USER}/{self.repo_id}", subfolder="unet") + for p1, p2 in zip(unet.parameters(), new_model.parameters()): + self.assertTrue(torch.equal(p1, p2)) + + # Reset repo + delete_repo(self.repo_id, token=TOKEN) + + def test_push_to_hub_in_organization(self): + components = self.get_pipeline_components() + pipeline = StableDiffusionPipeline(**components) + pipeline.push_to_hub(self.org_repo_id, token=TOKEN) + + new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id, subfolder="unet") + unet = components["unet"] + for p1, p2 in zip(unet.parameters(), new_model.parameters()): + self.assertTrue(torch.equal(p1, p2)) + + # Reset repo + delete_repo(token=TOKEN, repo_id=self.org_repo_id) + + # Push to hub via save_pretrained + with tempfile.TemporaryDirectory() as tmp_dir: + pipeline.save_pretrained(tmp_dir, push_to_hub=True, token=TOKEN, repo_id=self.org_repo_id) + + new_model = UNet2DConditionModel.from_pretrained(self.org_repo_id, subfolder="unet") + for p1, p2 in zip(unet.parameters(), new_model.parameters()): + self.assertTrue(torch.equal(p1, p2)) + + # Reset repo + delete_repo(self.org_repo_id, token=TOKEN) + + @unittest.skipIf( + not is_jinja_available(), + reason="Model card tests cannot be performed without Jinja installed.", + ) + def test_push_to_hub_library_name(self): + components = self.get_pipeline_components() + pipeline = StableDiffusionPipeline(**components) + pipeline.push_to_hub(self.repo_id, token=TOKEN) + + model_card = ModelCard.load(f"{USER}/{self.repo_id}", token=TOKEN).data + assert model_card.library_name == "diffusers" + + # Reset repo + delete_repo(self.repo_id, token=TOKEN) + + +class PyramidAttentionBroadcastTesterMixin: + pab_config = PyramidAttentionBroadcastConfig( + spatial_attention_block_skip_range=2, + spatial_attention_timestep_skip_range=(100, 800), + spatial_attention_block_identifiers=["transformer_blocks"], + ) + + def test_pyramid_attention_broadcast_layers(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + + num_layers = 0 + num_single_layers = 0 + dummy_component_kwargs = {} + dummy_component_parameters = inspect.signature(self.get_dummy_components).parameters + if "num_layers" in dummy_component_parameters: + num_layers = 2 + dummy_component_kwargs["num_layers"] = num_layers + if "num_single_layers" in dummy_component_parameters: + num_single_layers = 2 + dummy_component_kwargs["num_single_layers"] = num_single_layers + + components = self.get_dummy_components(**dummy_component_kwargs) + pipe = self.pipeline_class(**components) + pipe.set_progress_bar_config(disable=None) + + self.pab_config.current_timestep_callback = lambda: pipe.current_timestep + denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet + denoiser.enable_cache(self.pab_config) + + expected_hooks = 0 + if self.pab_config.spatial_attention_block_skip_range is not None: + expected_hooks += num_layers + num_single_layers + if self.pab_config.temporal_attention_block_skip_range is not None: + expected_hooks += num_layers + num_single_layers + if self.pab_config.cross_attention_block_skip_range is not None: + expected_hooks += num_layers + num_single_layers + + denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet + count = 0 + for module in denoiser.modules(): + if hasattr(module, "_diffusers_hook"): + hook = module._diffusers_hook.get_hook("pyramid_attention_broadcast") + if hook is None: + continue + count += 1 + self.assertTrue( + isinstance(hook, PyramidAttentionBroadcastHook), + "Hook should be of type PyramidAttentionBroadcastHook.", + ) + self.assertTrue(hook.state.cache is None, "Cache should be None at initialization.") + self.assertEqual(count, expected_hooks, "Number of hooks should match the expected number.") + + # Perform dummy inference step to ensure state is updated + def pab_state_check_callback(pipe, i, t, kwargs): + for module in denoiser.modules(): + if hasattr(module, "_diffusers_hook"): + hook = module._diffusers_hook.get_hook("pyramid_attention_broadcast") + if hook is None: + continue + self.assertTrue( + hook.state.cache is not None, + "Cache should have updated during inference.", + ) + self.assertTrue( + hook.state.iteration == i + 1, + "Hook iteration state should have updated during inference.", + ) + return {} + + inputs = self.get_dummy_inputs(device) + inputs["num_inference_steps"] = 2 + inputs["callback_on_step_end"] = pab_state_check_callback + pipe(**inputs)[0] + + # After inference, reset_stateful_hooks is called within the pipeline, which should have reset the states + for module in denoiser.modules(): + if hasattr(module, "_diffusers_hook"): + hook = module._diffusers_hook.get_hook("pyramid_attention_broadcast") + if hook is None: + continue + self.assertTrue( + hook.state.cache is None, + "Cache should be reset to None after inference.", + ) + self.assertTrue( + hook.state.iteration == 0, + "Iteration should be reset to 0 after inference.", + ) + + def test_pyramid_attention_broadcast_inference(self, expected_atol: float = 0.2): + # We need to use higher tolerance because we are using a random model. With a converged/trained + # model, the tolerance can be lower. + + device = "cpu" # ensure determinism for the device-dependent torch.Generator + num_layers = 2 + components = self.get_dummy_components(num_layers=num_layers) + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + pipe = pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + # Run inference without PAB + inputs = self.get_dummy_inputs(device) + inputs["num_inference_steps"] = 4 + output = pipe(**inputs)[0] + original_image_slice = output.flatten() + original_image_slice = np.concatenate((original_image_slice[:8], original_image_slice[-8:])) + + # Run inference with PAB enabled + self.pab_config.current_timestep_callback = lambda: pipe.current_timestep + denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet + denoiser.enable_cache(self.pab_config) + + inputs = self.get_dummy_inputs(device) + inputs["num_inference_steps"] = 4 + output = pipe(**inputs)[0] + image_slice_pab_enabled = output.flatten() + image_slice_pab_enabled = np.concatenate((image_slice_pab_enabled[:8], image_slice_pab_enabled[-8:])) + + # Run inference with PAB disabled + denoiser.disable_cache() + + inputs = self.get_dummy_inputs(device) + inputs["num_inference_steps"] = 4 + output = pipe(**inputs)[0] + image_slice_pab_disabled = output.flatten() + image_slice_pab_disabled = np.concatenate((image_slice_pab_disabled[:8], image_slice_pab_disabled[-8:])) + + assert np.allclose(original_image_slice, image_slice_pab_enabled, atol=expected_atol), ( + "PAB outputs should not differ much in specified timestep range." + ) + assert np.allclose(original_image_slice, image_slice_pab_disabled, atol=1e-4), ( + "Outputs from normal inference and after disabling cache should not differ." + ) + + +class FasterCacheTesterMixin: + faster_cache_config = FasterCacheConfig( + spatial_attention_block_skip_range=2, + spatial_attention_timestep_skip_range=(-1, 901), + unconditional_batch_skip_range=2, + attention_weight_callback=lambda _: 0.5, + ) + + def test_faster_cache_basic_warning_or_errors_raised(self): + components = self.get_dummy_components() + + logger = logging.get_logger("diffusers.hooks.faster_cache") + logger.setLevel(logging.INFO) + + # Check if warning is raise when no attention_weight_callback is provided + pipe = self.pipeline_class(**components) + with CaptureLogger(logger) as cap_logger: + config = FasterCacheConfig(spatial_attention_block_skip_range=2, attention_weight_callback=None) + apply_faster_cache(pipe.transformer, config) + self.assertTrue("No `attention_weight_callback` provided when enabling FasterCache" in cap_logger.out) + + # Check if error raised when unsupported tensor format used + pipe = self.pipeline_class(**components) + with self.assertRaises(ValueError): + config = FasterCacheConfig(spatial_attention_block_skip_range=2, tensor_format="BFHWC") + apply_faster_cache(pipe.transformer, config) + + def test_faster_cache_inference(self, expected_atol: float = 0.1): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + + def create_pipe(): + torch.manual_seed(0) + num_layers = 2 + components = self.get_dummy_components(num_layers=num_layers) + pipe = self.pipeline_class(**components) + pipe = pipe.to(device) + pipe.set_progress_bar_config(disable=None) + return pipe + + def run_forward(pipe): + torch.manual_seed(0) + inputs = self.get_dummy_inputs(device) + inputs["num_inference_steps"] = 4 + return pipe(**inputs)[0] + + # Run inference without FasterCache + pipe = create_pipe() + output = run_forward(pipe).flatten() + original_image_slice = np.concatenate((output[:8], output[-8:])) + + # Run inference with FasterCache enabled + self.faster_cache_config.current_timestep_callback = lambda: pipe.current_timestep + pipe = create_pipe() + pipe.transformer.enable_cache(self.faster_cache_config) + output = run_forward(pipe).flatten() + image_slice_faster_cache_enabled = np.concatenate((output[:8], output[-8:])) + + # Run inference with FasterCache disabled + pipe.transformer.disable_cache() + output = run_forward(pipe).flatten() + image_slice_faster_cache_disabled = np.concatenate((output[:8], output[-8:])) + + assert np.allclose(original_image_slice, image_slice_faster_cache_enabled, atol=expected_atol), ( + "FasterCache outputs should not differ much in specified timestep range." + ) + assert np.allclose(original_image_slice, image_slice_faster_cache_disabled, atol=1e-4), ( + "Outputs from normal inference and after disabling cache should not differ." + ) + + def test_faster_cache_state(self): + from diffusers.hooks.faster_cache import _FASTER_CACHE_BLOCK_HOOK, _FASTER_CACHE_DENOISER_HOOK + + device = "cpu" # ensure determinism for the device-dependent torch.Generator + num_layers = 0 + num_single_layers = 0 + dummy_component_kwargs = {} + dummy_component_parameters = inspect.signature(self.get_dummy_components).parameters + if "num_layers" in dummy_component_parameters: + num_layers = 2 + dummy_component_kwargs["num_layers"] = num_layers + if "num_single_layers" in dummy_component_parameters: + num_single_layers = 2 + dummy_component_kwargs["num_single_layers"] = num_single_layers + + components = self.get_dummy_components(**dummy_component_kwargs) + pipe = self.pipeline_class(**components) + pipe.set_progress_bar_config(disable=None) + + self.faster_cache_config.current_timestep_callback = lambda: pipe.current_timestep + pipe.transformer.enable_cache(self.faster_cache_config) + + expected_hooks = 0 + if self.faster_cache_config.spatial_attention_block_skip_range is not None: + expected_hooks += num_layers + num_single_layers + if self.faster_cache_config.temporal_attention_block_skip_range is not None: + expected_hooks += num_layers + num_single_layers + + # Check if faster_cache denoiser hook is attached + denoiser = pipe.transformer if hasattr(pipe, "transformer") else pipe.unet + self.assertTrue( + hasattr(denoiser, "_diffusers_hook") + and isinstance(denoiser._diffusers_hook.get_hook(_FASTER_CACHE_DENOISER_HOOK), FasterCacheDenoiserHook), + "Hook should be of type FasterCacheDenoiserHook.", + ) + + # Check if all blocks have faster_cache block hook attached + count = 0 + for name, module in denoiser.named_modules(): + if hasattr(module, "_diffusers_hook"): + if name == "": + # Skip the root denoiser module + continue + count += 1 + self.assertTrue( + isinstance(module._diffusers_hook.get_hook(_FASTER_CACHE_BLOCK_HOOK), FasterCacheBlockHook), + "Hook should be of type FasterCacheBlockHook.", + ) + self.assertEqual(count, expected_hooks, "Number of hooks should match expected number.") + + # Perform inference to ensure that states are updated correctly + def faster_cache_state_check_callback(pipe, i, t, kwargs): + for name, module in denoiser.named_modules(): + if not hasattr(module, "_diffusers_hook"): + continue + if name == "": + # Root denoiser module + state = module._diffusers_hook.get_hook(_FASTER_CACHE_DENOISER_HOOK).state + if not self.faster_cache_config.is_guidance_distilled: + self.assertTrue(state.low_frequency_delta is not None, "Low frequency delta should be set.") + self.assertTrue(state.high_frequency_delta is not None, "High frequency delta should be set.") + else: + # Internal blocks + state = module._diffusers_hook.get_hook(_FASTER_CACHE_BLOCK_HOOK).state + self.assertTrue(state.cache is not None and len(state.cache) == 2, "Cache should be set.") + self.assertTrue(state.iteration == i + 1, "Hook iteration state should have updated during inference.") + return {} + + inputs = self.get_dummy_inputs(device) + inputs["num_inference_steps"] = 4 + inputs["callback_on_step_end"] = faster_cache_state_check_callback + _ = pipe(**inputs)[0] + + # After inference, reset_stateful_hooks is called within the pipeline, which should have reset the states + for name, module in denoiser.named_modules(): + if not hasattr(module, "_diffusers_hook"): + continue + + if name == "": + # Root denoiser module + state = module._diffusers_hook.get_hook(_FASTER_CACHE_DENOISER_HOOK).state + self.assertTrue(state.iteration == 0, "Iteration should be reset to 0.") + self.assertTrue(state.low_frequency_delta is None, "Low frequency delta should be reset to None.") + self.assertTrue(state.high_frequency_delta is None, "High frequency delta should be reset to None.") + else: + # Internal blocks + state = module._diffusers_hook.get_hook(_FASTER_CACHE_BLOCK_HOOK).state + self.assertTrue(state.iteration == 0, "Iteration should be reset to 0.") + self.assertTrue(state.batch_size is None, "Batch size should be reset to None.") + self.assertTrue(state.cache is None, "Cache should be reset to None.") + + +# TODO(aryan, dhruv): the cache tester mixins should probably be rewritten so that more models can be tested out +# of the box once there is better cache support/implementation +class FirstBlockCacheTesterMixin: + # threshold is intentionally set higher than usual values since we're testing with random unconverged models + # that will not satisfy the expected properties of the denoiser for caching to be effective + first_block_cache_config = FirstBlockCacheConfig(threshold=0.8) + + def test_first_block_cache_inference(self, expected_atol: float = 0.1): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + + def create_pipe(): + torch.manual_seed(0) + num_layers = 2 + components = self.get_dummy_components(num_layers=num_layers) + pipe = self.pipeline_class(**components) + pipe = pipe.to(device) + pipe.set_progress_bar_config(disable=None) + return pipe + + def run_forward(pipe): + torch.manual_seed(0) + inputs = self.get_dummy_inputs(device) + inputs["num_inference_steps"] = 4 + return pipe(**inputs)[0] + + # Run inference without FirstBlockCache + pipe = create_pipe() + output = run_forward(pipe).flatten() + original_image_slice = np.concatenate((output[:8], output[-8:])) + + # Run inference with FirstBlockCache enabled + pipe = create_pipe() + pipe.transformer.enable_cache(self.first_block_cache_config) + output = run_forward(pipe).flatten() + image_slice_fbc_enabled = np.concatenate((output[:8], output[-8:])) + + # Run inference with FirstBlockCache disabled + pipe.transformer.disable_cache() + output = run_forward(pipe).flatten() + image_slice_fbc_disabled = np.concatenate((output[:8], output[-8:])) + + assert np.allclose(original_image_slice, image_slice_fbc_enabled, atol=expected_atol), ( + "FirstBlockCache outputs should not differ much." + ) + assert np.allclose(original_image_slice, image_slice_fbc_disabled, atol=1e-4), ( + "Outputs from normal inference and after disabling cache should not differ." + ) + + +class TaylorSeerCacheTesterMixin: + taylorseer_cache_config = TaylorSeerCacheConfig( + cache_interval=5, + disable_cache_before_step=10, + max_order=1, + taylor_factors_dtype=torch.bfloat16, + use_lite_mode=True, + ) + + def test_taylorseer_cache_inference(self, expected_atol: float = 0.1): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + + def create_pipe(): + torch.manual_seed(0) + num_layers = 2 + components = self.get_dummy_components(num_layers=num_layers) + pipe = self.pipeline_class(**components) + pipe = pipe.to(device) + pipe.set_progress_bar_config(disable=None) + return pipe + + def run_forward(pipe): + torch.manual_seed(0) + inputs = self.get_dummy_inputs(device) + inputs["num_inference_steps"] = 50 + return pipe(**inputs)[0] + + # Run inference without TaylorSeerCache + pipe = create_pipe() + output = run_forward(pipe).flatten() + original_image_slice = np.concatenate((output[:8], output[-8:])) + + # Run inference with TaylorSeerCache enabled + pipe = create_pipe() + pipe.transformer.enable_cache(self.taylorseer_cache_config) + output = run_forward(pipe).flatten() + image_slice_fbc_enabled = np.concatenate((output[:8], output[-8:])) + + # Run inference with TaylorSeerCache disabled + pipe.transformer.disable_cache() + output = run_forward(pipe).flatten() + image_slice_fbc_disabled = np.concatenate((output[:8], output[-8:])) + + assert np.allclose(original_image_slice, image_slice_fbc_enabled, atol=expected_atol), ( + "TaylorSeerCache outputs should not differ much." + ) + assert np.allclose(original_image_slice, image_slice_fbc_disabled, atol=1e-4), ( + "Outputs from normal inference and after disabling cache should not differ." + ) + + +class MagCacheTesterMixin: + mag_cache_config = MagCacheConfig( + threshold=0.06, + max_skip_steps=3, + retention_ratio=0.2, + num_inference_steps=50, + mag_ratios=torch.ones(50), + ) + + def test_mag_cache_inference(self, expected_atol: float = 0.1): + device = "cpu" + + def create_pipe(): + torch.manual_seed(0) + num_layers = 2 + components = self.get_dummy_components(num_layers=num_layers) + pipe = self.pipeline_class(**components) + pipe = pipe.to(device) + pipe.set_progress_bar_config(disable=None) + return pipe + + def run_forward(pipe): + torch.manual_seed(0) + inputs = self.get_dummy_inputs(device) + # Match the config steps + inputs["num_inference_steps"] = 50 + return pipe(**inputs)[0] + + # 1. Run inference without MagCache (Baseline) + pipe = create_pipe() + output = run_forward(pipe).flatten() + original_image_slice = np.concatenate((output[:8], output[-8:])) + + # 2. Run inference with MagCache ENABLED + pipe = create_pipe() + pipe.transformer.enable_cache(self.mag_cache_config) + output = run_forward(pipe).flatten() + image_slice_enabled = np.concatenate((output[:8], output[-8:])) + + # 3. Run inference with MagCache DISABLED + pipe.transformer.disable_cache() + output = run_forward(pipe).flatten() + image_slice_disabled = np.concatenate((output[:8], output[-8:])) + + assert np.allclose(original_image_slice, image_slice_enabled, atol=expected_atol), ( + "MagCache outputs should not differ too much from baseline." + ) + + assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-4), ( + "Outputs after disabling cache should match original inference exactly." + ) + + +# Some models (e.g. unCLIP) are extremely likely to significantly deviate depending on which hardware is used. +# This helper function is used to check that the image doesn't deviate on average more than 10 pixels from a +# reference image. +def assert_mean_pixel_difference(image, expected_image, expected_max_diff=10): + image = np.asarray(DiffusionPipeline.numpy_to_pil(image)[0], dtype=np.float32) + expected_image = np.asarray(DiffusionPipeline.numpy_to_pil(expected_image)[0], dtype=np.float32) + avg_diff = np.abs(image - expected_image).mean() + assert avg_diff < expected_max_diff, f"Error image deviates {avg_diff} pixels on average" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_onnx_common.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_onnx_common.py new file mode 100644 index 0000000000000000000000000000000000000000..fa077efb8ab0f122631f58b158580e4d501c1661 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/test_pipelines_onnx_common.py @@ -0,0 +1,12 @@ +from ..testing_utils import require_onnxruntime + + +@require_onnxruntime +class OnnxPipelineTesterMixin: + """ + This mixin is designed to be used with unittest.TestCase classes. + It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline, + equivalence of dict and tuple outputs, etc. + """ + + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/visualcloze/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/visualcloze/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/visualcloze/test_pipeline_visualcloze_combined.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/visualcloze/test_pipeline_visualcloze_combined.py new file mode 100644 index 0000000000000000000000000000000000000000..bfbcd141d9b318e119b7f3f2eb73313e2e25a36b --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/visualcloze/test_pipeline_visualcloze_combined.py @@ -0,0 +1,352 @@ +import random +import tempfile +import unittest + +import numpy as np +import torch +from PIL import Image +from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel + +import diffusers +from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, FluxTransformer2DModel, VisualClozePipeline +from diffusers.utils import logging + +from ...testing_utils import ( + CaptureLogger, + enable_full_determinism, + floats_tensor, + require_accelerator, + torch_device, +) +from ..test_pipelines_common import PipelineTesterMixin, to_np + + +enable_full_determinism() + + +class VisualClozePipelineFastTests(unittest.TestCase, PipelineTesterMixin): + pipeline_class = VisualClozePipeline + params = frozenset( + [ + "task_prompt", + "content_prompt", + "upsampling_height", + "upsampling_width", + "guidance_scale", + "prompt_embeds", + "pooled_prompt_embeds", + "upsampling_strength", + ] + ) + batch_params = frozenset(["task_prompt", "content_prompt", "image"]) + test_xformers_attention = False + test_layerwise_casting = True + test_group_offloading = True + + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + transformer = FluxTransformer2DModel( + patch_size=1, + in_channels=12, + out_channels=4, + num_layers=1, + num_single_layers=1, + attention_head_dim=6, + num_attention_heads=2, + joint_attention_dim=32, + pooled_projection_dim=32, + axes_dims_rope=[2, 2, 2], + ) + clip_text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + hidden_act="gelu", + projection_dim=32, + ) + + torch.manual_seed(0) + text_encoder = CLIPTextModel(clip_text_encoder_config) + + torch.manual_seed(0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder_2 = T5EncoderModel(config) + + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + vae = AutoencoderKL( + sample_size=32, + in_channels=3, + out_channels=3, + block_out_channels=(4,), + layers_per_block=1, + latent_channels=1, + norm_num_groups=1, + use_quant_conv=False, + use_post_quant_conv=False, + shift_factor=0.0609, + scaling_factor=1.5035, + ) + + scheduler = FlowMatchEulerDiscreteScheduler() + + return { + "scheduler": scheduler, + "text_encoder": text_encoder, + "text_encoder_2": text_encoder_2, + "tokenizer": tokenizer, + "tokenizer_2": tokenizer_2, + "transformer": transformer, + "vae": vae, + "resolution": 32, + } + + def get_dummy_inputs(self, device, seed=0): + # Create example images to simulate the input format required by VisualCloze + context_image = [ + Image.fromarray(floats_tensor((32, 32, 3), rng=random.Random(seed), scale=255).numpy().astype(np.uint8)) + for _ in range(2) + ] + query_image = [ + Image.fromarray( + floats_tensor((32, 32, 3), rng=random.Random(seed + 1), scale=255).numpy().astype(np.uint8) + ), + None, + ] + + # Create an image list that conforms to the VisualCloze input format + image = [ + context_image, # In-Context example + query_image, # Query image + ] + + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device="cpu").manual_seed(seed) + + inputs = { + "task_prompt": "Each row outlines a logical process, starting from [IMAGE1] gray-based depth map with detailed object contours, to achieve [IMAGE2] an image with flawless clarity.", + "content_prompt": "A beautiful landscape with mountains and a lake", + "image": image, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 5.0, + "upsampling_height": 32, + "upsampling_width": 32, + "max_sequence_length": 77, + "output_type": "np", + "upsampling_strength": 0.4, + } + return inputs + + def test_visualcloze_different_prompts(self): + pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device) + + inputs = self.get_dummy_inputs(torch_device) + output_same_prompt = pipe(**inputs).images[0] + + inputs = self.get_dummy_inputs(torch_device) + inputs["task_prompt"] = "A different task to perform." + output_different_prompts = pipe(**inputs).images[0] + + max_diff = np.abs(output_same_prompt - output_different_prompts).max() + + # Outputs should be different + assert max_diff > 1e-6 + + def test_visualcloze_image_output_shape(self): + pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device) + inputs = self.get_dummy_inputs(torch_device) + + height_width_pairs = [(32, 32), (72, 57)] + for height, width in height_width_pairs: + expected_height = height - height % (pipe.generation_pipe.vae_scale_factor * 2) + expected_width = width - width % (pipe.generation_pipe.vae_scale_factor * 2) + + inputs.update({"upsampling_height": height, "upsampling_width": width}) + image = pipe(**inputs).images[0] + output_height, output_width, _ = image.shape + assert (output_height, output_width) == (expected_height, expected_width) + + def test_inference_batch_single_identical(self): + self._test_inference_batch_single_identical(expected_max_diff=1e-3) + + def test_upsampling_strength(self, expected_min_diff=1e-1): + pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device) + inputs = self.get_dummy_inputs(torch_device) + + # Test different upsampling strengths + inputs["upsampling_strength"] = 0.2 + output_no_upsampling = pipe(**inputs).images[0] + + inputs["upsampling_strength"] = 0.8 + output_full_upsampling = pipe(**inputs).images[0] + + # Different upsampling strengths should produce different outputs + max_diff = np.abs(output_no_upsampling - output_full_upsampling).max() + assert max_diff > expected_min_diff + + def test_different_task_prompts(self, expected_min_diff=1e-1): + pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device) + inputs = self.get_dummy_inputs(torch_device) + + output_original = pipe(**inputs).images[0] + + inputs["task_prompt"] = "A different task description for image generation" + output_different_task = pipe(**inputs).images[0] + + # Different task prompts should produce different outputs + max_diff = np.abs(output_original - output_different_task).max() + assert max_diff > expected_min_diff + + @unittest.skip( + "Test not applicable because the pipeline being tested is a wrapper pipeline. CFG tests should be done on the inner pipelines." + ) + def test_callback_cfg(self): + pass + + def test_save_load_local(self, expected_max_difference=1e-3): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0] + + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + logger.setLevel(diffusers.logging.INFO) + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + + with CaptureLogger(logger) as cap_logger: + # NOTE: Resolution must be set to 32 for loading otherwise will lead to OOM on CI hardware + # This attribute is not serialized in the config of the pipeline + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, resolution=32) + + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + for name in pipe_loaded.components.keys(): + if name not in pipe_loaded._optional_components: + assert name in str(cap_logger) + + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, expected_max_difference) + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + if not hasattr(self.pipeline_class, "_optional_components"): + return + components = self.get_dummy_components() + for key in components: + if "text_encoder" in key and hasattr(components[key], "eval"): + components[key].eval() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + # set all optional components to None + for optional_component in pipe._optional_components: + setattr(pipe, optional_component, None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + # NOTE: Resolution must be set to 32 for loading otherwise will lead to OOM on CI hardware + # This attribute is not serialized in the config of the pipeline + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, resolution=32) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for optional_component in pipe._optional_components: + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, expected_max_difference) + + @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU") + @require_accelerator + def test_save_load_float16(self, expected_max_diff=1e-2): + components = self.get_dummy_components() + for name, module in components.items(): + if hasattr(module, "half"): + components[name] = module.to(torch_device).half() + + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + # NOTE: Resolution must be set to 32 for loading otherwise will lead to OOM on CI hardware + # This attribute is not serialized in the config of the pipeline + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16, resolution=32) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for name, component in pipe_loaded.components.items(): + if hasattr(component, "dtype"): + self.assertTrue( + component.dtype == torch.float16, + f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.", + ) + + inputs = self.get_dummy_inputs(torch_device) + output_loaded = pipe_loaded(**inputs)[0] + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess( + max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading." + ) + + @unittest.skip("Test not supported.") + def test_pipeline_with_accelerator_device_map(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/visualcloze/test_pipeline_visualcloze_generation.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/visualcloze/test_pipeline_visualcloze_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..5640f2f634d39ce786749b5ddc90633d2f8535ac --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/visualcloze/test_pipeline_visualcloze_generation.py @@ -0,0 +1,317 @@ +import random +import tempfile +import unittest + +import numpy as np +import torch +from PIL import Image +from transformers import AutoConfig, AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel + +import diffusers +from diffusers import ( + AutoencoderKL, + FlowMatchEulerDiscreteScheduler, + FluxTransformer2DModel, + VisualClozeGenerationPipeline, +) +from diffusers.utils import logging + +from ...testing_utils import ( + CaptureLogger, + enable_full_determinism, + floats_tensor, + require_accelerator, + torch_device, +) +from ..test_pipelines_common import PipelineTesterMixin, to_np + + +enable_full_determinism() + + +class VisualClozeGenerationPipelineFastTests(unittest.TestCase, PipelineTesterMixin): + pipeline_class = VisualClozeGenerationPipeline + params = frozenset( + [ + "task_prompt", + "content_prompt", + "guidance_scale", + "prompt_embeds", + "pooled_prompt_embeds", + ] + ) + batch_params = frozenset(["task_prompt", "content_prompt", "image"]) + test_xformers_attention = False + test_layerwise_casting = True + test_group_offloading = True + + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + transformer = FluxTransformer2DModel( + patch_size=1, + in_channels=12, + out_channels=4, + num_layers=1, + num_single_layers=1, + attention_head_dim=6, + num_attention_heads=2, + joint_attention_dim=32, + pooled_projection_dim=32, + axes_dims_rope=[2, 2, 2], + ) + clip_text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + hidden_act="gelu", + projection_dim=32, + ) + + torch.manual_seed(0) + text_encoder = CLIPTextModel(clip_text_encoder_config) + + torch.manual_seed(0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder_2 = T5EncoderModel(config) + + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + vae = AutoencoderKL( + sample_size=32, + in_channels=3, + out_channels=3, + block_out_channels=(4,), + layers_per_block=1, + latent_channels=1, + norm_num_groups=1, + use_quant_conv=False, + use_post_quant_conv=False, + shift_factor=0.0609, + scaling_factor=1.5035, + ) + + scheduler = FlowMatchEulerDiscreteScheduler() + + return { + "scheduler": scheduler, + "text_encoder": text_encoder, + "text_encoder_2": text_encoder_2, + "tokenizer": tokenizer, + "tokenizer_2": tokenizer_2, + "transformer": transformer, + "vae": vae, + "resolution": 32, + } + + def get_dummy_inputs(self, device, seed=0): + # Create example images to simulate the input format required by VisualCloze + context_image = [ + Image.fromarray(floats_tensor((32, 32, 3), rng=random.Random(seed), scale=255).numpy().astype(np.uint8)) + for _ in range(2) + ] + query_image = [ + Image.fromarray( + floats_tensor((32, 32, 3), rng=random.Random(seed + 1), scale=255).numpy().astype(np.uint8) + ), + None, + ] + + # Create an image list that conforms to the VisualCloze input format + image = [ + context_image, # In-Context example + query_image, # Query image + ] + + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device="cpu").manual_seed(seed) + + inputs = { + "task_prompt": "Each row outlines a logical process, starting from [IMAGE1] gray-based depth map with detailed object contours, to achieve [IMAGE2] an image with flawless clarity.", + "content_prompt": "A beautiful landscape with mountains and a lake", + "image": image, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 5.0, + "max_sequence_length": 77, + "output_type": "np", + } + return inputs + + def test_visualcloze_different_prompts(self): + pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device) + + inputs = self.get_dummy_inputs(torch_device) + output_same_prompt = pipe(**inputs).images[0] + + inputs = self.get_dummy_inputs(torch_device) + inputs["task_prompt"] = "A different task to perform." + output_different_prompts = pipe(**inputs).images[0] + + max_diff = np.abs(output_same_prompt - output_different_prompts).max() + + # Outputs should be different + assert max_diff > 1e-6 + + def test_inference_batch_single_identical(self): + self._test_inference_batch_single_identical(expected_max_diff=1e-3) + + def test_different_task_prompts(self, expected_min_diff=1e-1): + pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device) + inputs = self.get_dummy_inputs(torch_device) + + output_original = pipe(**inputs).images[0] + + inputs["task_prompt"] = "A different task description for image generation" + output_different_task = pipe(**inputs).images[0] + + # Different task prompts should produce different outputs + max_diff = np.abs(output_original - output_different_task).max() + assert max_diff > expected_min_diff + + def test_save_load_local(self, expected_max_difference=5e-4): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0] + + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + logger.setLevel(diffusers.logging.INFO) + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + + with CaptureLogger(logger) as cap_logger: + # NOTE: Resolution must be set to 32 for loading otherwise will lead to OOM on CI hardware + # This attribute is not serialized in the config of the pipeline + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, resolution=32) + + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + + for name in pipe_loaded.components.keys(): + if name not in pipe_loaded._optional_components: + assert name in str(cap_logger) + + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, expected_max_difference) + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + if not hasattr(self.pipeline_class, "_optional_components"): + return + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + # set all optional components to None + for optional_component in pipe._optional_components: + setattr(pipe, optional_component, None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + # NOTE: Resolution must be set to 32 for loading otherwise will lead to OOM on CI hardware + # This attribute is not serialized in the config of the pipeline + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, resolution=32) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for optional_component in pipe._optional_components: + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, expected_max_difference) + + @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU") + @require_accelerator + def test_save_load_float16(self, expected_max_diff=1e-2): + components = self.get_dummy_components() + for name, module in components.items(): + if hasattr(module, "half"): + components[name] = module.to(torch_device).half() + + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + # NOTE: Resolution must be set to 32 for loading otherwise will lead to OOM on CI hardware + # This attribute is not serialized in the config of the pipeline + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16, resolution=32) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for name, component in pipe_loaded.components.items(): + if hasattr(component, "dtype"): + self.assertTrue( + component.dtype == torch.float16, + f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.", + ) + + inputs = self.get_dummy_inputs(torch_device) + output_loaded = pipe_loaded(**inputs)[0] + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess( + max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading." + ) + + @unittest.skip("Skipped due to missing layout_prompt. Needs further investigation.") + def test_encode_prompt_works_in_isolation(self, extra_required_param_value_dict=None, atol=0.0001, rtol=0.0001): + pass + + @unittest.skip("Needs to be revisited later.") + def test_pipeline_with_accelerator_device_map(self, expected_max_difference=0.0001): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan.py new file mode 100644 index 0000000000000000000000000000000000000000..958e1b8c8eaf702cf2b865ee565e8744516b5294 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan.py @@ -0,0 +1,202 @@ +# Copyright 2025 The HuggingFace Team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import tempfile +import unittest + +import numpy as np +import torch +from transformers import AutoConfig, AutoTokenizer, T5EncoderModel + +from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, WanPipeline, WanTransformer3DModel + +from ...testing_utils import ( + backend_empty_cache, + enable_full_determinism, + require_torch_accelerator, + slow, + torch_device, +) +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin + + +enable_full_determinism() + + +class WanPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=16, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + # TODO: impl FlowDPMSolverMultistepScheduler + scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=16, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + ) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "transformer_2": None, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + inputs = { + "prompt": "dance monkey", + "negative_prompt": "negative", # TODO + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + "height": 16, + "width": 16, + "num_frames": 9, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames + generated_video = video[0] + self.assertEqual(generated_video.shape, (9, 3, 16, 16)) + + # fmt: off + expected_slice = torch.tensor([0.4525, 0.452, 0.4485, 0.4534, 0.4524, 0.4529, 0.454, 0.453, 0.5127, 0.5326, 0.5204, 0.5253, 0.5439, 0.5424, 0.5133, 0.5078]) + # fmt: on + + generated_slice = generated_video.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3)) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + # _optional_components include transformer, transformer_2, but only transformer_2 is optional for this wan2.1 t2v pipeline + def test_save_load_optional_components(self, expected_max_difference=1e-4): + optional_component = "transformer_2" + + components = self.get_dummy_components() + components[optional_component] = None + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max() + self.assertLess(max_diff, expected_max_difference) + + +@slow +@require_torch_accelerator +class WanPipelineIntegrationTests(unittest.TestCase): + prompt = "A painting of a squirrel eating a burger." + + def setUp(self): + super().setUp() + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + super().tearDown() + gc.collect() + backend_empty_cache(torch_device) + + @unittest.skip("TODO: test needs to be implemented") + def test_Wanx(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_22.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_22.py new file mode 100644 index 0000000000000000000000000000000000000000..fd17ca414af42baf99a80051efce0f46becd1aa6 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_22.py @@ -0,0 +1,366 @@ +# Copyright 2025 The HuggingFace Team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tempfile +import unittest + +import numpy as np +import torch +from transformers import AutoConfig, AutoTokenizer, T5EncoderModel + +from diffusers import AutoencoderKLWan, UniPCMultistepScheduler, WanPipeline, WanTransformer3DModel + +from ...testing_utils import enable_full_determinism, torch_device +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin + + +enable_full_determinism() + + +class Wan22PipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=16, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + scheduler = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=16, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + ) + + torch.manual_seed(0) + transformer_2 = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=16, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + ) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "transformer_2": transformer_2, + "boundary_ratio": 0.875, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + inputs = { + "prompt": "dance monkey", + "negative_prompt": "negative", + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + "height": 16, + "width": 16, + "num_frames": 9, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class( + **components, + ) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames + generated_video = video[0] + self.assertEqual(generated_video.shape, (9, 3, 16, 16)) + + # fmt: off + expected_slice = torch.tensor([0.4525, 0.452, 0.4485, 0.4534, 0.4524, 0.4529, 0.454, 0.453, 0.5127, 0.5326, 0.5204, 0.5253, 0.5439, 0.5424, 0.5133, 0.5078]) + # fmt: on + + generated_slice = generated_video.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3)) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + optional_component = "transformer" + + components = self.get_dummy_components() + components[optional_component] = None + components["boundary_ratio"] = 1.0 # for wan 2.2 14B, transformer is not used when boundary_ratio is 1.0 + + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + self.assertTrue( + getattr(pipe_loaded, "transformer") is None, + "`transformer` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max() + self.assertLess(max_diff, expected_max_difference) + + +class Wan225BPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=48, + in_channels=12, + out_channels=12, + is_residual=True, + patch_size=2, + latents_mean=[0.0] * 48, + latents_std=[1.0] * 48, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + scale_factor_spatial=16, + scale_factor_temporal=4, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + scheduler = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=48, + out_channels=48, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + ) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "transformer_2": None, + "boundary_ratio": None, + "expand_timesteps": True, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + inputs = { + "prompt": "dance monkey", + "negative_prompt": "negative", # TODO + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + "height": 32, + "width": 32, + "num_frames": 9, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class( + **components, + ) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames + generated_video = video[0] + self.assertEqual(generated_video.shape, (9, 3, 32, 32)) + + # fmt: off + expected_slice = torch.tensor([[[0.4814, 0.4298, 0.5094, 0.4289, 0.5061, 0.4301, 0.5043, 0.4284, 0.5375, + 0.5965, 0.5527, 0.6014, 0.5228, 0.6076, 0.6644, 0.5651]]]) + # fmt: on + + generated_slice = generated_video.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue( + torch.allclose(generated_slice, expected_slice, atol=1e-3), + f"generated_slice: {generated_slice}, expected_slice: {expected_slice}", + ) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + def test_components_function(self): + init_components = self.get_dummy_components() + init_components.pop("boundary_ratio") + init_components.pop("expand_timesteps") + pipe = self.pipeline_class(**init_components) + + self.assertTrue(hasattr(pipe, "components")) + self.assertTrue(set(pipe.components.keys()) == set(init_components.keys())) + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + optional_component = "transformer_2" + + components = self.get_dummy_components() + components[optional_component] = None + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max() + self.assertLess(max_diff, expected_max_difference) + + def test_inference_batch_single_identical(self): + self._test_inference_batch_single_identical(expected_max_diff=2e-3) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_22_image_to_video.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_22_image_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..4634047ebb736838d6a584c285a2f44cf2bb8631 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_22_image_to_video.py @@ -0,0 +1,394 @@ +# Copyright 2025 The HuggingFace Team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tempfile +import unittest + +import numpy as np +import torch +from PIL import Image +from transformers import AutoConfig, AutoTokenizer, T5EncoderModel + +from diffusers import AutoencoderKLWan, UniPCMultistepScheduler, WanImageToVideoPipeline, WanTransformer3DModel + +from ...testing_utils import ( + enable_full_determinism, + torch_device, +) +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin + + +enable_full_determinism() + + +class Wan22ImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanImageToVideoPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=16, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + scheduler = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=36, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + ) + + torch.manual_seed(0) + transformer_2 = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=36, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + ) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "transformer_2": transformer_2, + "image_encoder": None, + "image_processor": None, + "boundary_ratio": 0.875, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + image_height = 16 + image_width = 16 + image = Image.new("RGB", (image_width, image_height)) + inputs = { + "image": image, + "prompt": "dance monkey", + "negative_prompt": "negative", # TODO + "height": image_height, + "width": image_width, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + "num_frames": 9, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class( + **components, + ) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames + generated_video = video[0] + self.assertEqual(generated_video.shape, (9, 3, 16, 16)) + + # fmt: off + expected_slice = torch.tensor([0.4527, 0.4526, 0.4498, 0.4539, 0.4521, 0.4524, 0.4533, 0.4535, 0.5154, + 0.5353, 0.5200, 0.5174, 0.5434, 0.5301, 0.5199, 0.5216]) + # fmt: on + + generated_slice = generated_video.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue( + torch.allclose(generated_slice, expected_slice, atol=1e-3), + f"generated_slice: {generated_slice}, expected_slice: {expected_slice}", + ) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + optional_component = ["transformer", "image_encoder", "image_processor"] + + components = self.get_dummy_components() + for component in optional_component: + components[component] = None + components["boundary_ratio"] = 1.0 # for wan 2.2 14B, transformer is not used when boundary_ratio is 1.0 + + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for component in optional_component: + self.assertTrue( + getattr(pipe_loaded, component) is None, + f"`{component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max() + self.assertLess(max_diff, expected_max_difference) + + +class Wan225BImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanImageToVideoPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=48, + in_channels=12, + out_channels=12, + is_residual=True, + patch_size=2, + latents_mean=[0.0] * 48, + latents_std=[1.0] * 48, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + scale_factor_spatial=16, + scale_factor_temporal=4, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + scheduler = UniPCMultistepScheduler(prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=48, + out_channels=48, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + ) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "transformer_2": None, + "image_encoder": None, + "image_processor": None, + "boundary_ratio": None, + "expand_timesteps": True, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + image_height = 32 + image_width = 32 + image = Image.new("RGB", (image_width, image_height)) + inputs = { + "image": image, + "prompt": "dance monkey", + "negative_prompt": "negative", # TODO + "height": image_height, + "width": image_width, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + "num_frames": 9, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class( + **components, + ) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames + generated_video = video[0] + self.assertEqual(generated_video.shape, (9, 3, 32, 32)) + + # fmt: off + expected_slice = torch.tensor([[0.4833, 0.4305, 0.5100, 0.4299, 0.5056, 0.4298, 0.5052, 0.4332, 0.5550, + 0.6092, 0.5536, 0.5928, 0.5199, 0.5864, 0.6705, 0.5493]]) + # fmt: on + + generated_slice = generated_video.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue( + torch.allclose(generated_slice, expected_slice, atol=1e-3), + f"generated_slice: {generated_slice}, expected_slice: {expected_slice}", + ) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + def test_components_function(self): + init_components = self.get_dummy_components() + init_components.pop("boundary_ratio") + init_components.pop("expand_timesteps") + pipe = self.pipeline_class(**init_components) + + self.assertTrue(hasattr(pipe, "components")) + self.assertTrue(set(pipe.components.keys()) == set(init_components.keys())) + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + optional_component = ["transformer_2", "image_encoder", "image_processor"] + + components = self.get_dummy_components() + for component in optional_component: + components[component] = None + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for component in optional_component: + self.assertTrue( + getattr(pipe_loaded, component) is None, + f"`{component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max() + self.assertLess(max_diff, expected_max_difference) + + def test_inference_batch_single_identical(self): + self._test_inference_batch_single_identical(expected_max_diff=2e-3) + + @unittest.skip("Test not supported") + def test_callback_inputs(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_animate.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_animate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d634fb7184997595d47ab63e2098f8f7bd946a1 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_animate.py @@ -0,0 +1,241 @@ +# Copyright 2025 The HuggingFace Team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import unittest + +import numpy as np +import torch +from PIL import Image +from transformers import ( + AutoConfig, + AutoTokenizer, + CLIPImageProcessor, + CLIPVisionConfig, + CLIPVisionModelWithProjection, + T5EncoderModel, +) + +from diffusers import ( + AutoencoderKLWan, + FlowMatchEulerDiscreteScheduler, + WanAnimatePipeline, + WanAnimateTransformer3DModel, +) + +from ...testing_utils import ( + backend_empty_cache, + enable_full_determinism, + require_torch_accelerator, + slow, + torch_device, +) +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin + + +enable_full_determinism() + + +class WanAnimatePipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanAnimatePipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=16, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + channel_sizes = {"4": 16, "8": 16, "16": 16} + transformer = WanAnimateTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=36, + latent_channels=16, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + image_dim=4, + rope_max_seq_len=32, + motion_encoder_channel_sizes=channel_sizes, + motion_encoder_size=16, + motion_style_dim=8, + motion_dim=4, + motion_encoder_dim=16, + face_encoder_hidden_dim=16, + face_encoder_num_heads=2, + inject_face_latents_blocks=2, + ) + + torch.manual_seed(0) + image_encoder_config = CLIPVisionConfig( + hidden_size=4, + projection_dim=4, + num_hidden_layers=2, + num_attention_heads=2, + image_size=4, + intermediate_size=16, + patch_size=1, + ) + image_encoder = CLIPVisionModelWithProjection(image_encoder_config) + + torch.manual_seed(0) + image_processor = CLIPImageProcessor(crop_size=4, size=4) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "image_encoder": image_encoder, + "image_processor": image_processor, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + + num_frames = 17 + height = 16 + width = 16 + face_height = 16 + face_width = 16 + + image = Image.new("RGB", (height, width)) + pose_video = [Image.new("RGB", (height, width))] * num_frames + face_video = [Image.new("RGB", (face_height, face_width))] * num_frames + + inputs = { + "image": image, + "pose_video": pose_video, + "face_video": face_video, + "prompt": "dance monkey", + "negative_prompt": "negative", + "height": height, + "width": width, + "segment_frame_length": 77, # TODO: can we set this to num_frames? + "num_inference_steps": 2, + "mode": "animate", + "prev_segment_conditioning_frames": 1, + "generator": generator, + "guidance_scale": 1.0, + "output_type": "pt", + "max_sequence_length": 16, + } + return inputs + + def test_inference(self): + """Test basic inference in animation mode.""" + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames[0] + self.assertEqual(video.shape, (17, 3, 16, 16)) + + expected_video = torch.randn(17, 3, 16, 16) + max_diff = np.abs(video - expected_video).max() + self.assertLessEqual(max_diff, 1e10) + + def test_inference_replacement(self): + """Test the pipeline in replacement mode with background and mask videos.""" + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs["mode"] = "replace" + num_frames = 17 + height = 16 + width = 16 + inputs["background_video"] = [Image.new("RGB", (height, width))] * num_frames + inputs["mask_video"] = [Image.new("L", (height, width))] * num_frames + + video = pipe(**inputs).frames[0] + self.assertEqual(video.shape, (17, 3, 16, 16)) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + @unittest.skip( + "Setting the Wan Animate latents to zero at the last denoising step does not guarantee that the output will be" + " zero. I believe this is because the latents are further processed in the outer loop where we loop over" + " inference segments." + ) + def test_callback_inputs(self): + pass + + +@slow +@require_torch_accelerator +class WanAnimatePipelineIntegrationTests(unittest.TestCase): + prompt = "A painting of a squirrel eating a burger." + + def setUp(self): + super().setUp() + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + super().tearDown() + gc.collect() + backend_empty_cache(torch_device) + + @unittest.skip("TODO: test needs to be implemented") + def test_wan_animate(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_image_to_video.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_image_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..7ed263abdcb57fd760274d95d8af5e6ce4811766 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_image_to_video.py @@ -0,0 +1,384 @@ +# Copyright 2025 The HuggingFace Team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tempfile +import unittest + +import numpy as np +import torch +from PIL import Image +from transformers import ( + AutoConfig, + AutoTokenizer, + CLIPImageProcessor, + CLIPVisionConfig, + CLIPVisionModelWithProjection, + T5EncoderModel, +) + +from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, WanImageToVideoPipeline, WanTransformer3DModel + +from ...testing_utils import enable_full_determinism, torch_device +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin + + +enable_full_determinism() + + +class WanImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanImageToVideoPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs", "height", "width"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=16, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + # TODO: impl FlowDPMSolverMultistepScheduler + scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=36, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + image_dim=4, + ) + + torch.manual_seed(0) + image_encoder_config = CLIPVisionConfig( + hidden_size=4, + projection_dim=4, + num_hidden_layers=2, + num_attention_heads=2, + image_size=32, + intermediate_size=16, + patch_size=1, + ) + image_encoder = CLIPVisionModelWithProjection(image_encoder_config) + + torch.manual_seed(0) + image_processor = CLIPImageProcessor(crop_size=32, size=32) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "image_encoder": image_encoder, + "image_processor": image_processor, + "transformer_2": None, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + image_height = 16 + image_width = 16 + image = Image.new("RGB", (image_width, image_height)) + inputs = { + "image": image, + "prompt": "dance monkey", + "negative_prompt": "negative", # TODO + "height": image_height, + "width": image_width, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + "num_frames": 9, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames + generated_video = video[0] + self.assertEqual(generated_video.shape, (9, 3, 16, 16)) + + # fmt: off + expected_slice = torch.tensor([0.4525, 0.4525, 0.4497, 0.4536, 0.452, 0.4529, 0.454, 0.4535, 0.5072, 0.5527, 0.5165, 0.5244, 0.5481, 0.5282, 0.5208, 0.5214]) + # fmt: on + + generated_slice = generated_video.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3)) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + @unittest.skip("TODO: revisit failing as it requires a very high threshold to pass") + def test_inference_batch_single_identical(self): + pass + + # _optional_components include transformer, transformer_2 and image_encoder, image_processor, but only transformer_2 is optional for wan2.1 i2v pipeline + def test_save_load_optional_components(self, expected_max_difference=1e-4): + optional_component = "transformer_2" + + components = self.get_dummy_components() + components[optional_component] = None + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max() + self.assertLess(max_diff, expected_max_difference) + + +class WanFLFToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanImageToVideoPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs", "height", "width"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=16, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + # TODO: impl FlowDPMSolverMultistepScheduler + scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=36, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + image_dim=4, + pos_embed_seq_len=2 * (4 * 4 + 1), + ) + + torch.manual_seed(0) + image_encoder_config = CLIPVisionConfig( + hidden_size=4, + projection_dim=4, + num_hidden_layers=2, + num_attention_heads=2, + image_size=4, + intermediate_size=16, + patch_size=1, + ) + image_encoder = CLIPVisionModelWithProjection(image_encoder_config) + + torch.manual_seed(0) + image_processor = CLIPImageProcessor(crop_size=4, size=4) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "image_encoder": image_encoder, + "image_processor": image_processor, + "transformer_2": None, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + image_height = 16 + image_width = 16 + image = Image.new("RGB", (image_width, image_height)) + last_image = Image.new("RGB", (image_width, image_height)) + inputs = { + "image": image, + "last_image": last_image, + "prompt": "dance monkey", + "negative_prompt": "negative", + "height": image_height, + "width": image_width, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + "num_frames": 9, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames + generated_video = video[0] + self.assertEqual(generated_video.shape, (9, 3, 16, 16)) + + # fmt: off + expected_slice = torch.tensor([0.4531, 0.4527, 0.4498, 0.4542, 0.4526, 0.4527, 0.4534, 0.4534, 0.5061, 0.5185, 0.5283, 0.5181, 0.5309, 0.5365, 0.5113, 0.5244]) + # fmt: on + + generated_slice = generated_video.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3)) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + @unittest.skip("TODO: revisit failing as it requires a very high threshold to pass") + def test_inference_batch_single_identical(self): + pass + + # _optional_components include transformer, transformer_2 and image_encoder, image_processor, but only transformer_2 is optional for wan2.1 FLFT2V pipeline + def test_save_load_optional_components(self, expected_max_difference=1e-4): + optional_component = "transformer_2" + + components = self.get_dummy_components() + components[optional_component] = None + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max() + self.assertLess(max_diff, expected_max_difference) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_vace.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_vace.py new file mode 100644 index 0000000000000000000000000000000000000000..53becce1685dd301f687c86e8b7a11a77657569f --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_vace.py @@ -0,0 +1,300 @@ +# Copyright 2025 The HuggingFace Team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tempfile +import unittest + +import numpy as np +import torch +from PIL import Image +from transformers import AutoConfig, AutoTokenizer, T5EncoderModel + +from diffusers import ( + AutoencoderKLWan, + FlowMatchEulerDiscreteScheduler, + UniPCMultistepScheduler, + WanVACEPipeline, + WanVACETransformer3DModel, +) + +from ...testing_utils import enable_full_determinism, torch_device +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin + + +enable_full_determinism() + + +class WanVACEPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanVACEPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=16, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanVACETransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=16, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=3, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + vace_layers=[0, 2], + vace_in_channels=96, + ) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "transformer_2": None, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + + num_frames = 17 + height = 16 + width = 16 + + video = [Image.new("RGB", (height, width))] * num_frames + mask = [Image.new("L", (height, width), 0)] * num_frames + + inputs = { + "video": video, + "mask": mask, + "prompt": "dance monkey", + "negative_prompt": "negative", + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + "height": 16, + "width": 16, + "num_frames": num_frames, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames[0] + self.assertEqual(video.shape, (17, 3, 16, 16)) + + # fmt: off + expected_slice = [0.4523, 0.45198, 0.44872, 0.45326, 0.45211, 0.45258, 0.45344, 0.453, 0.52431, 0.52572, 0.50701, 0.5118, 0.53717, 0.53093, 0.50557, 0.51402] + # fmt: on + + video_slice = video.flatten() + video_slice = torch.cat([video_slice[:8], video_slice[-8:]]) + video_slice = [round(x, 5) for x in video_slice.tolist()] + self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3)) + + def test_inference_with_single_reference_image(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs["reference_images"] = Image.new("RGB", (16, 16)) + video = pipe(**inputs).frames[0] + self.assertEqual(video.shape, (17, 3, 16, 16)) + + # fmt: off + expected_slice = [0.45247, 0.45214, 0.44874, 0.45314, 0.45171, 0.45299, 0.45428, 0.45317, 0.51378, 0.52658, 0.53361, 0.52303, 0.46204, 0.50435, 0.52555, 0.51342] + # fmt: on + + video_slice = video.flatten() + video_slice = torch.cat([video_slice[:8], video_slice[-8:]]) + video_slice = [round(x, 5) for x in video_slice.tolist()] + self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3)) + + def test_inference_with_multiple_reference_image(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs["reference_images"] = [[Image.new("RGB", (16, 16))] * 2] + video = pipe(**inputs).frames[0] + self.assertEqual(video.shape, (17, 3, 16, 16)) + + # fmt: off + expected_slice = [0.45321, 0.45221, 0.44818, 0.45375, 0.45268, 0.4519, 0.45271, 0.45253, 0.51244, 0.52223, 0.51253, 0.51321, 0.50743, 0.51177, 0.51626, 0.50983] + # fmt: on + + video_slice = video.flatten() + video_slice = torch.cat([video_slice[:8], video_slice[-8:]]) + video_slice = [round(x, 5) for x in video_slice.tolist()] + self.assertTrue(np.allclose(video_slice, expected_slice, atol=1e-3)) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + @unittest.skip("Errors out because passing multiple prompts at once is not yet supported by this pipeline.") + def test_encode_prompt_works_in_isolation(self): + pass + + @unittest.skip("Batching is not yet supported with this pipeline") + def test_inference_batch_consistent(self): + pass + + @unittest.skip("Batching is not yet supported with this pipeline") + def test_inference_batch_single_identical(self): + return super().test_inference_batch_single_identical() + + @unittest.skip( + "AutoencoderKLWan encoded latents are always in FP32. This test is not designed to handle mixed dtype inputs" + ) + def test_float16_inference(self): + pass + + @unittest.skip( + "AutoencoderKLWan encoded latents are always in FP32. This test is not designed to handle mixed dtype inputs" + ) + def test_save_load_float16(self): + pass + + def test_inference_with_only_transformer(self): + components = self.get_dummy_components() + components["transformer_2"] = None + components["boundary_ratio"] = 0.0 + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + video = pipe(**inputs).frames[0] + assert video.shape == (17, 3, 16, 16) + + def test_inference_with_only_transformer_2(self): + components = self.get_dummy_components() + components["transformer_2"] = components["transformer"] + components["transformer"] = None + + # FlowMatchEulerDiscreteScheduler doesn't support running low noise only scheduler + # because starting timestep t == 1000 == boundary_timestep + components["scheduler"] = UniPCMultistepScheduler( + prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0 + ) + + components["boundary_ratio"] = 1.0 + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + video = pipe(**inputs).frames[0] + assert video.shape == (17, 3, 16, 16) + + def test_save_load_optional_components(self, expected_max_difference=1e-4): + optional_component = ["transformer"] + + components = self.get_dummy_components() + components["transformer_2"] = components["transformer"] + # FlowMatchEulerDiscreteScheduler doesn't support running low noise only scheduler + # because starting timestep t == 1000 == boundary_timestep + components["scheduler"] = UniPCMultistepScheduler( + prediction_type="flow_prediction", use_flow_sigmas=True, flow_shift=3.0 + ) + for component in optional_component: + components[component] = None + + components["boundary_ratio"] = 1.0 + + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir, safe_serialization=False) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + for component in pipe_loaded.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for component in optional_component: + assert getattr(pipe_loaded, component) is None, f"`{component}` did not stay set to None after loading." + + inputs = self.get_dummy_inputs(generator_device) + torch.manual_seed(0) + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max() + assert max_diff < expected_max_difference, "Outputs exceed expecpted maximum difference" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_video_to_video.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_video_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..3804e972b97f1565180b7ded3f4a9e134b4a9dd1 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/wan/test_wan_video_to_video.py @@ -0,0 +1,150 @@ +# Copyright 2025 The HuggingFace Team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import torch +from PIL import Image +from transformers import AutoConfig, AutoTokenizer, T5EncoderModel + +from diffusers import AutoencoderKLWan, UniPCMultistepScheduler, WanTransformer3DModel, WanVideoToVideoPipeline + +from ...testing_utils import ( + enable_full_determinism, +) +from ..pipeline_params import TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import ( + PipelineTesterMixin, +) + + +enable_full_determinism() + + +class WanVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = WanVideoToVideoPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = frozenset(["video", "prompt", "negative_prompt"]) + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + supports_dduf = False + + def get_dummy_components(self): + torch.manual_seed(0) + vae = AutoencoderKLWan( + base_dim=3, + z_dim=16, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temperal_downsample=[False, True, True], + ) + + torch.manual_seed(0) + scheduler = UniPCMultistepScheduler(flow_shift=3.0) + config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5") + text_encoder = T5EncoderModel(config) + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + torch.manual_seed(0) + transformer = WanTransformer3DModel( + patch_size=(1, 2, 2), + num_attention_heads=2, + attention_head_dim=12, + in_channels=16, + out_channels=16, + text_dim=32, + freq_dim=256, + ffn_dim=32, + num_layers=2, + cross_attn_norm=True, + qk_norm="rms_norm_across_heads", + rope_max_seq_len=32, + ) + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + + video = [Image.new("RGB", (16, 16))] * 17 + inputs = { + "video": video, + "prompt": "dance monkey", + "negative_prompt": "negative", # TODO + "generator": generator, + "num_inference_steps": 4, + "guidance_scale": 6.0, + "height": 16, + "width": 16, + "max_sequence_length": 16, + "output_type": "pt", + } + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + video = pipe(**inputs).frames + generated_video = video[0] + self.assertEqual(generated_video.shape, (17, 3, 16, 16)) + + # fmt: off + expected_slice = torch.tensor([0.4522, 0.4534, 0.4532, 0.4553, 0.4526, 0.4538, 0.4533, 0.4547, 0.513, 0.5176, 0.5286, 0.4958, 0.4955, 0.5381, 0.5154, 0.5195]) + # fmt:on + + generated_slice = generated_video.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3)) + + @unittest.skip("Test not supported") + def test_attention_slicing_forward_pass(self): + pass + + @unittest.skip( + "WanVideoToVideoPipeline has to run in mixed precision. Casting the entire pipeline will result in errors" + ) + def test_float16_inference(self): + pass + + @unittest.skip( + "WanVideoToVideoPipeline has to run in mixed precision. Save/Load the entire pipeline in FP16 will result in errors" + ) + def test_save_load_float16(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image.py new file mode 100644 index 0000000000000000000000000000000000000000..79a5fa0de5f018c33ad7f57f93d61803d3245660 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image.py @@ -0,0 +1,307 @@ +# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import os +import unittest + +import numpy as np +import torch +from transformers import Qwen2Tokenizer, Qwen3Config, Qwen3Model + +from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, ZImagePipeline, ZImageTransformer2DModel + +from ...testing_utils import torch_device +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin, to_np + + +# Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations +# Cannot use enable_full_determinism() which sets it to True +os.environ["CUDA_LAUNCH_BLOCKING"] = "1" +os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" +torch.use_deterministic_algorithms(False) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False +if hasattr(torch.backends, "cuda"): + torch.backends.cuda.matmul.allow_tf32 = False + +# Note: Some tests (test_float16_inference, test_save_load_float16) may fail in full suite +# due to RopeEmbedder cache state pollution between tests. They pass when run individually. +# This is a known test isolation issue, not a functional bug. + + +class ZImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = ZImagePipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + supports_dduf = False + test_xformers_attention = False + test_layerwise_casting = True + test_group_offloading = True + + def setUp(self): + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + def tearDown(self): + super().tearDown() + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + def get_dummy_components(self): + torch.manual_seed(0) + transformer = ZImageTransformer2DModel( + all_patch_size=(2,), + all_f_patch_size=(1,), + in_channels=16, + dim=32, + n_layers=2, + n_refiner_layers=1, + n_heads=2, + n_kv_heads=2, + norm_eps=1e-5, + qk_norm=True, + cap_feat_dim=16, + rope_theta=256.0, + t_scale=1000.0, + axes_dims=[8, 4, 4], + axes_lens=[256, 32, 32], + ) + # `x_pad_token` and `cap_pad_token` are initialized with `torch.empty`. + # This can cause NaN data values in our testing environment. Fixating them + # helps prevent that issue. + with torch.no_grad(): + transformer.x_pad_token.copy_(torch.ones_like(transformer.x_pad_token.data)) + transformer.cap_pad_token.copy_(torch.ones_like(transformer.cap_pad_token.data)) + + torch.manual_seed(0) + vae = AutoencoderKL( + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + block_out_channels=[32, 64], + layers_per_block=1, + latent_channels=16, + norm_num_groups=32, + sample_size=32, + scaling_factor=0.3611, + shift_factor=0.1159, + ) + + torch.manual_seed(0) + scheduler = FlowMatchEulerDiscreteScheduler() + + torch.manual_seed(0) + config = Qwen3Config( + hidden_size=16, + intermediate_size=16, + num_hidden_layers=2, + num_attention_heads=2, + num_key_value_heads=2, + vocab_size=151936, + max_position_embeddings=512, + ) + text_encoder = Qwen3Model(config) + tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration") + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + + inputs = { + "prompt": "dance monkey", + "negative_prompt": "bad quality", + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 3.0, + "cfg_normalization": False, + "cfg_truncation": 1.0, + "height": 32, + "width": 32, + "max_sequence_length": 16, + "output_type": "pt", + } + + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + image = pipe(**inputs).images + generated_image = image[0] + self.assertEqual(generated_image.shape, (3, 32, 32)) + + # fmt: off + expected_slice = torch.tensor([0.4622, 0.4532, 0.4714, 0.5087, 0.5371, 0.5405, 0.4492, 0.4479, 0.2984, 0.2783, 0.5409, 0.6577, 0.3952, 0.5524, 0.5262, 0.453]) + # fmt: on + + generated_slice = generated_image.flatten() + generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]]) + self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=5e-2)) + + def test_inference_batch_single_identical(self): + self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1) + + def test_num_images_per_prompt(self): + import inspect + + sig = inspect.signature(self.pipeline_class.__call__) + + if "num_images_per_prompt" not in sig.parameters: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + batch_sizes = [1, 2] + num_images_per_prompts = [1, 2] + + for batch_size in batch_sizes: + for num_images_per_prompt in num_images_per_prompts: + inputs = self.get_dummy_inputs(torch_device) + + for key in inputs.keys(): + if key in self.batch_params: + inputs[key] = batch_size * [inputs[key]] + + images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0] + + assert images.shape[0] == batch_size * num_images_per_prompt + + del pipe + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_attention_slicing_forward_pass( + self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 + ): + if not self.test_attention_slicing: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + output_without_slicing = pipe(**inputs)[0] + + pipe.enable_attention_slicing(slice_size=1) + inputs = self.get_dummy_inputs(generator_device) + output_with_slicing1 = pipe(**inputs)[0] + + pipe.enable_attention_slicing(slice_size=2) + inputs = self.get_dummy_inputs(generator_device) + output_with_slicing2 = pipe(**inputs)[0] + + if test_max_difference: + max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max() + max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max() + self.assertLess( + max(max_diff1, max_diff2), + expected_max_diff, + "Attention slicing should not affect the inference results", + ) + + def test_vae_tiling(self, expected_diff_max: float = 0.2): + generator_device = "cpu" + components = self.get_dummy_components() + + pipe = self.pipeline_class(**components) + pipe.to("cpu") + pipe.set_progress_bar_config(disable=None) + + # Without tiling + inputs = self.get_dummy_inputs(generator_device) + inputs["height"] = inputs["width"] = 128 + output_without_tiling = pipe(**inputs)[0] + + # With tiling (standard AutoencoderKL doesn't accept parameters) + pipe.vae.enable_tiling() + inputs = self.get_dummy_inputs(generator_device) + inputs["height"] = inputs["width"] = 128 + output_with_tiling = pipe(**inputs)[0] + + self.assertLess( + (to_np(output_without_tiling) - to_np(output_with_tiling)).max(), + expected_diff_max, + "VAE tiling should not affect the inference results", + ) + + def test_pipeline_with_accelerator_device_map(self, expected_max_difference=5e-4): + # Z-Image RoPE embeddings (complex64) have slightly higher numerical tolerance + super().test_pipeline_with_accelerator_device_map(expected_max_difference=expected_max_difference) + + def test_group_offloading_inference(self): + # Block-level offloading conflicts with RoPE cache. Pipeline-level offloading (tested separately) works fine. + self.skipTest("Using test_pipeline_level_group_offloading_inference instead") + + def test_save_load_float16(self, expected_max_diff=1e-2): + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + super().test_save_load_float16(expected_max_diff=expected_max_diff) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image_img2img.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image_img2img.py new file mode 100644 index 0000000000000000000000000000000000000000..91b3025b17e8cd677734308ca7e4c02e40af0507 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image_img2img.py @@ -0,0 +1,358 @@ +# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import os +import unittest + +import numpy as np +import torch +from transformers import Qwen2Tokenizer, Qwen3Config, Qwen3Model + +from diffusers import ( + AutoencoderKL, + FlowMatchEulerDiscreteScheduler, + ZImageImg2ImgPipeline, + ZImageTransformer2DModel, +) +from diffusers.utils.testing_utils import floats_tensor + +from ...testing_utils import torch_device +from ..pipeline_params import ( + IMAGE_TO_IMAGE_IMAGE_PARAMS, + TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, + TEXT_GUIDED_IMAGE_VARIATION_PARAMS, +) +from ..test_pipelines_common import PipelineTesterMixin, to_np + + +# Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations +# Cannot use enable_full_determinism() which sets it to True +# Note: Z-Image does not support FP16 inference due to complex64 RoPE embeddings +os.environ["CUDA_LAUNCH_BLOCKING"] = "1" +os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" +torch.use_deterministic_algorithms(False) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False +if hasattr(torch.backends, "cuda"): + torch.backends.cuda.matmul.allow_tf32 = False + + +class ZImageImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = ZImageImg2ImgPipeline + params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS + image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS + image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "strength", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + supports_dduf = False + test_xformers_attention = False + test_layerwise_casting = True + test_group_offloading = True + + def setUp(self): + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + def tearDown(self): + super().tearDown() + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + def get_dummy_components(self): + torch.manual_seed(0) + transformer = ZImageTransformer2DModel( + all_patch_size=(2,), + all_f_patch_size=(1,), + in_channels=16, + dim=32, + n_layers=2, + n_refiner_layers=1, + n_heads=2, + n_kv_heads=2, + norm_eps=1e-5, + qk_norm=True, + cap_feat_dim=16, + rope_theta=256.0, + t_scale=1000.0, + axes_dims=[8, 4, 4], + axes_lens=[256, 32, 32], + ) + + torch.manual_seed(0) + vae = AutoencoderKL( + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + block_out_channels=[32, 64], + layers_per_block=1, + latent_channels=16, + norm_num_groups=32, + sample_size=32, + scaling_factor=0.3611, + shift_factor=0.1159, + ) + + torch.manual_seed(0) + scheduler = FlowMatchEulerDiscreteScheduler() + + torch.manual_seed(0) + config = Qwen3Config( + hidden_size=16, + intermediate_size=16, + num_hidden_layers=2, + num_attention_heads=2, + num_key_value_heads=2, + vocab_size=151936, + max_position_embeddings=512, + ) + text_encoder = Qwen3Model(config) + tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration") + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + } + return components + + def get_dummy_inputs(self, device, seed=0): + import random + + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + + image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) + + inputs = { + "prompt": "dance monkey", + "negative_prompt": "bad quality", + "image": image, + "strength": 0.6, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 3.0, + "cfg_normalization": False, + "cfg_truncation": 1.0, + "height": 32, + "width": 32, + "max_sequence_length": 16, + "output_type": "np", + } + + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + image = pipe(**inputs).images + generated_image = image[0] + self.assertEqual(generated_image.shape, (32, 32, 3)) + + def test_inference_batch_single_identical(self): + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1) + + def test_num_images_per_prompt(self): + import inspect + + sig = inspect.signature(self.pipeline_class.__call__) + + if "num_images_per_prompt" not in sig.parameters: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + batch_sizes = [1, 2] + num_images_per_prompts = [1, 2] + + for batch_size in batch_sizes: + for num_images_per_prompt in num_images_per_prompts: + inputs = self.get_dummy_inputs(torch_device) + + for key in inputs.keys(): + if key in self.batch_params: + inputs[key] = batch_size * [inputs[key]] + + images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0] + + assert images.shape[0] == batch_size * num_images_per_prompt + + del pipe + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_attention_slicing_forward_pass( + self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 + ): + if not self.test_attention_slicing: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + output_without_slicing = pipe(**inputs)[0] + + pipe.enable_attention_slicing(slice_size=1) + inputs = self.get_dummy_inputs(generator_device) + output_with_slicing1 = pipe(**inputs)[0] + + pipe.enable_attention_slicing(slice_size=2) + inputs = self.get_dummy_inputs(generator_device) + output_with_slicing2 = pipe(**inputs)[0] + + if test_max_difference: + max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max() + max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max() + self.assertLess( + max(max_diff1, max_diff2), + expected_max_diff, + "Attention slicing should not affect the inference results", + ) + + def test_vae_tiling(self, expected_diff_max: float = 0.3): + import random + + generator_device = "cpu" + components = self.get_dummy_components() + + pipe = self.pipeline_class(**components) + pipe.to("cpu") + pipe.set_progress_bar_config(disable=None) + + # Without tiling + inputs = self.get_dummy_inputs(generator_device) + inputs["height"] = inputs["width"] = 128 + # Generate a larger image for the input + inputs["image"] = floats_tensor((1, 3, 128, 128), rng=random.Random(0)).to("cpu") + output_without_tiling = pipe(**inputs)[0] + + # With tiling (standard AutoencoderKL doesn't accept parameters) + pipe.vae.enable_tiling() + inputs = self.get_dummy_inputs(generator_device) + inputs["height"] = inputs["width"] = 128 + inputs["image"] = floats_tensor((1, 3, 128, 128), rng=random.Random(0)).to("cpu") + output_with_tiling = pipe(**inputs)[0] + + self.assertLess( + (to_np(output_without_tiling) - to_np(output_with_tiling)).max(), + expected_diff_max, + "VAE tiling should not affect the inference results", + ) + + def test_pipeline_with_accelerator_device_map(self, expected_max_difference=5e-4): + # Z-Image RoPE embeddings (complex64) have slightly higher numerical tolerance + super().test_pipeline_with_accelerator_device_map(expected_max_difference=expected_max_difference) + + def test_group_offloading_inference(self): + # Block-level offloading conflicts with RoPE cache. Pipeline-level offloading (tested separately) works fine. + self.skipTest("Using test_pipeline_level_group_offloading_inference instead") + + def test_save_load_float16(self, expected_max_diff=1e-2): + # Z-Image does not support FP16 due to complex64 RoPE embeddings + self.skipTest("Z-Image does not support FP16 inference") + + def test_float16_inference(self, expected_max_diff=5e-2): + # Z-Image does not support FP16 due to complex64 RoPE embeddings + self.skipTest("Z-Image does not support FP16 inference") + + def test_strength_parameter(self): + """Test that strength parameter affects the output correctly.""" + device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + # Test with different strength values + inputs_low_strength = self.get_dummy_inputs(device) + inputs_low_strength["strength"] = 0.2 + + inputs_high_strength = self.get_dummy_inputs(device) + inputs_high_strength["strength"] = 0.8 + + # Both should complete without errors + output_low = pipe(**inputs_low_strength).images[0] + output_high = pipe(**inputs_high_strength).images[0] + + # Outputs should be different (different amount of transformation) + self.assertFalse(np.allclose(output_low, output_high, atol=1e-3)) + + def test_invalid_strength(self): + """Test that invalid strength values raise appropriate errors.""" + device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + + inputs = self.get_dummy_inputs(device) + + # Test strength < 0 + inputs["strength"] = -0.1 + with self.assertRaises(ValueError): + pipe(**inputs) + + # Test strength > 1 + inputs["strength"] = 1.5 + with self.assertRaises(ValueError): + pipe(**inputs) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image_inpaint.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image_inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..e904a4e44bd782421fe399d84be3374d453bf173 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/pipelines/z_image/test_z_image_inpaint.py @@ -0,0 +1,396 @@ +# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import os +import unittest + +import numpy as np +import torch +from transformers import Qwen2Tokenizer, Qwen3Config, Qwen3Model + +from diffusers import ( + AutoencoderKL, + FlowMatchEulerDiscreteScheduler, + ZImageInpaintPipeline, + ZImageTransformer2DModel, +) +from diffusers.utils.testing_utils import floats_tensor + +from ...testing_utils import torch_device +from ..pipeline_params import ( + IMAGE_TO_IMAGE_IMAGE_PARAMS, + TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, + TEXT_GUIDED_IMAGE_INPAINTING_PARAMS, +) +from ..test_pipelines_common import PipelineTesterMixin, to_np + + +# Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations +# Cannot use enable_full_determinism() which sets it to True +# Note: Z-Image does not support FP16 inference due to complex64 RoPE embeddings +os.environ["CUDA_LAUNCH_BLOCKING"] = "1" +os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" +torch.use_deterministic_algorithms(False) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False +if hasattr(torch.backends, "cuda"): + torch.backends.cuda.matmul.allow_tf32 = False + + +class ZImageInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = ZImageInpaintPipeline + params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS + image_params = frozenset(["image", "mask_image"]) + image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "strength", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + supports_dduf = False + test_xformers_attention = False + test_layerwise_casting = True + test_group_offloading = True + + def setUp(self): + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + def tearDown(self): + super().tearDown() + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + def get_dummy_components(self): + torch.manual_seed(0) + transformer = ZImageTransformer2DModel( + all_patch_size=(2,), + all_f_patch_size=(1,), + in_channels=16, + dim=32, + n_layers=2, + n_refiner_layers=1, + n_heads=2, + n_kv_heads=2, + norm_eps=1e-5, + qk_norm=True, + cap_feat_dim=16, + rope_theta=256.0, + t_scale=1000.0, + axes_dims=[8, 4, 4], + axes_lens=[256, 32, 32], + ) + # `x_pad_token` and `cap_pad_token` are initialized with `torch.empty` which contains + # uninitialized memory. Set them to known values for deterministic test behavior. + with torch.no_grad(): + transformer.x_pad_token.copy_(torch.ones_like(transformer.x_pad_token.data)) + transformer.cap_pad_token.copy_(torch.ones_like(transformer.cap_pad_token.data)) + + torch.manual_seed(0) + vae = AutoencoderKL( + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + block_out_channels=[32, 64], + layers_per_block=1, + latent_channels=16, + norm_num_groups=32, + sample_size=32, + scaling_factor=0.3611, + shift_factor=0.1159, + ) + + torch.manual_seed(0) + scheduler = FlowMatchEulerDiscreteScheduler() + + torch.manual_seed(0) + config = Qwen3Config( + hidden_size=16, + intermediate_size=16, + num_hidden_layers=2, + num_attention_heads=2, + num_key_value_heads=2, + vocab_size=151936, + max_position_embeddings=512, + ) + text_encoder = Qwen3Model(config) + tokenizer = Qwen2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-Qwen2VLForConditionalGeneration") + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + } + return components + + def get_dummy_inputs(self, device, seed=0): + import random + + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + + image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) + # Create mask: 1 = inpaint region, 0 = preserve region + mask_image = torch.zeros((1, 1, 32, 32), device=device) + mask_image[:, :, 8:24, 8:24] = 1.0 # Inpaint center region + + inputs = { + "prompt": "dance monkey", + "negative_prompt": "bad quality", + "image": image, + "mask_image": mask_image, + "strength": 1.0, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 3.0, + "cfg_normalization": False, + "cfg_truncation": 1.0, + "height": 32, + "width": 32, + "max_sequence_length": 16, + "output_type": "np", + } + + return inputs + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + image = pipe(**inputs).images + generated_image = image[0] + self.assertEqual(generated_image.shape, (32, 32, 3)) + + def test_inference_batch_single_identical(self): + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-1) + + def test_num_images_per_prompt(self): + import inspect + + sig = inspect.signature(self.pipeline_class.__call__) + + if "num_images_per_prompt" not in sig.parameters: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + batch_sizes = [1, 2] + num_images_per_prompts = [1, 2] + + for batch_size in batch_sizes: + for num_images_per_prompt in num_images_per_prompts: + inputs = self.get_dummy_inputs(torch_device) + + for key in inputs.keys(): + if key in self.batch_params: + inputs[key] = batch_size * [inputs[key]] + + images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0] + + assert images.shape[0] == batch_size * num_images_per_prompt + + del pipe + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def test_attention_slicing_forward_pass( + self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3 + ): + if not self.test_attention_slicing: + return + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + for component in pipe.components.values(): + if hasattr(component, "set_default_attn_processor"): + component.set_default_attn_processor() + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator_device = "cpu" + inputs = self.get_dummy_inputs(generator_device) + output_without_slicing = pipe(**inputs)[0] + + pipe.enable_attention_slicing(slice_size=1) + inputs = self.get_dummy_inputs(generator_device) + output_with_slicing1 = pipe(**inputs)[0] + + pipe.enable_attention_slicing(slice_size=2) + inputs = self.get_dummy_inputs(generator_device) + output_with_slicing2 = pipe(**inputs)[0] + + if test_max_difference: + max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max() + max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max() + self.assertLess( + max(max_diff1, max_diff2), + expected_max_diff, + "Attention slicing should not affect the inference results", + ) + + def test_vae_tiling(self, expected_diff_max: float = 0.7): + import random + + generator_device = "cpu" + components = self.get_dummy_components() + + pipe = self.pipeline_class(**components) + pipe.to("cpu") + pipe.set_progress_bar_config(disable=None) + + # Without tiling + inputs = self.get_dummy_inputs(generator_device) + inputs["height"] = inputs["width"] = 128 + # Generate a larger image for the input + inputs["image"] = floats_tensor((1, 3, 128, 128), rng=random.Random(0)).to("cpu") + # Generate a larger mask for the input + mask = torch.zeros((1, 1, 128, 128), device="cpu") + mask[:, :, 32:96, 32:96] = 1.0 + inputs["mask_image"] = mask + output_without_tiling = pipe(**inputs)[0] + + # With tiling (standard AutoencoderKL doesn't accept parameters) + pipe.vae.enable_tiling() + inputs = self.get_dummy_inputs(generator_device) + inputs["height"] = inputs["width"] = 128 + inputs["image"] = floats_tensor((1, 3, 128, 128), rng=random.Random(0)).to("cpu") + inputs["mask_image"] = mask + output_with_tiling = pipe(**inputs)[0] + + self.assertLess( + (to_np(output_without_tiling) - to_np(output_with_tiling)).max(), + expected_diff_max, + "VAE tiling should not affect the inference results", + ) + + def test_pipeline_with_accelerator_device_map(self, expected_max_difference=1e-3): + # Z-Image RoPE embeddings (complex64) have slightly higher numerical tolerance + # Inpainting mask blending adds additional numerical variance + super().test_pipeline_with_accelerator_device_map(expected_max_difference=expected_max_difference) + + def test_group_offloading_inference(self): + # Block-level offloading conflicts with RoPE cache. Pipeline-level offloading (tested separately) works fine. + self.skipTest("Using test_pipeline_level_group_offloading_inference instead") + + def test_save_load_float16(self, expected_max_diff=1e-2): + # Z-Image does not support FP16 due to complex64 RoPE embeddings + self.skipTest("Z-Image does not support FP16 inference") + + def test_float16_inference(self, expected_max_diff=5e-2): + # Z-Image does not support FP16 due to complex64 RoPE embeddings + self.skipTest("Z-Image does not support FP16 inference") + + def test_strength_parameter(self): + """Test that strength parameter affects the output correctly.""" + device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + # Test with different strength values + inputs_low_strength = self.get_dummy_inputs(device) + inputs_low_strength["strength"] = 0.2 + + inputs_high_strength = self.get_dummy_inputs(device) + inputs_high_strength["strength"] = 0.8 + + # Both should complete without errors + output_low = pipe(**inputs_low_strength).images[0] + output_high = pipe(**inputs_high_strength).images[0] + + # Outputs should be different (different amount of transformation) + self.assertFalse(np.allclose(output_low, output_high, atol=1e-3)) + + def test_invalid_strength(self): + """Test that invalid strength values raise appropriate errors.""" + device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + + inputs = self.get_dummy_inputs(device) + + # Test strength < 0 + inputs["strength"] = -0.1 + with self.assertRaises(ValueError): + pipe(**inputs) + + # Test strength > 1 + inputs["strength"] = 1.5 + with self.assertRaises(ValueError): + pipe(**inputs) + + def test_mask_inpainting(self): + """Test that the mask properly controls which regions are inpainted.""" + device = "cpu" + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + # Generate with full mask (inpaint everything) + inputs_full = self.get_dummy_inputs(device) + inputs_full["mask_image"] = torch.ones((1, 1, 32, 32), device=device) + + # Generate with no mask (preserve everything) + inputs_none = self.get_dummy_inputs(device) + inputs_none["mask_image"] = torch.zeros((1, 1, 32, 32), device=device) + + # Both should complete without errors + output_full = pipe(**inputs_full).images[0] + output_none = pipe(**inputs_none).images[0] + + # Outputs should be different (full inpaint vs preserve) + self.assertFalse(np.allclose(output_full, output_none, atol=1e-3)) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/README.md b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f1585581597d77c0f6d465d84d9d836741dd1671 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/README.md @@ -0,0 +1,44 @@ +The tests here are adapted from [`transformers` tests](https://github.com/huggingface/transformers/tree/409fcfdfccde77a14b7cc36972b774cabc371ae1/tests/quantization/bnb). + +They were conducted on the `audace` machine, using a single RTX 4090. Below is `nvidia-smi`: + +```bash ++-----------------------------------------------------------------------------------------+ +| NVIDIA-SMI 550.90.07 Driver Version: 550.90.07 CUDA Version: 12.4 | +|-----------------------------------------+------------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+========================+======================| +| 0 NVIDIA GeForce RTX 4090 Off | 00000000:01:00.0 Off | Off | +| 30% 55C P0 61W / 450W | 1MiB / 24564MiB | 2% Default | +| | | N/A | ++-----------------------------------------+------------------------+----------------------+ +| 1 NVIDIA GeForce RTX 4090 Off | 00000000:13:00.0 Off | Off | +| 30% 51C P0 60W / 450W | 1MiB / 24564MiB | 0% Default | +| | | N/A | ++-----------------------------------------+------------------------+----------------------+ +``` + +`diffusers-cli`: + +```bash +- 🤗 Diffusers version: 0.31.0.dev0 +- Platform: Linux-5.15.0-117-generic-x86_64-with-glibc2.35 +- Running on Google Colab?: No +- Python version: 3.10.12 +- PyTorch version (GPU?): 2.5.0.dev20240818+cu124 (True) +- Flax version (CPU?/GPU?/TPU?): not installed (NA) +- Jax version: not installed +- JaxLib version: not installed +- Huggingface_hub version: 0.24.5 +- Transformers version: 4.44.2 +- Accelerate version: 0.34.0.dev0 +- PEFT version: 0.12.0 +- Bitsandbytes version: 0.43.3 +- Safetensors version: 0.4.4 +- xFormers version: not installed +- Accelerator: NVIDIA GeForce RTX 4090, 24564 MiB +NVIDIA GeForce RTX 4090, 24564 MiB +- Using GPU in script?: Yes +``` \ No newline at end of file diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/test_4bit.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/test_4bit.py new file mode 100644 index 0000000000000000000000000000000000000000..c1da8f1ece78021c4c68de461e57cd492ad19db7 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/test_4bit.py @@ -0,0 +1,895 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a clone of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import gc +import os +import tempfile +import unittest + +import numpy as np +import pytest +import safetensors.torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from diffusers import ( + BitsAndBytesConfig, + DiffusionPipeline, + FluxControlPipeline, + FluxTransformer2DModel, + SD3Transformer2DModel, +) +from diffusers.quantizers import PipelineQuantizationConfig +from diffusers.utils import is_accelerate_version, logging + +from ...testing_utils import ( + CaptureLogger, + backend_empty_cache, + is_bitsandbytes_available, + is_torch_available, + is_transformers_available, + load_pt, + numpy_cosine_similarity_distance, + require_accelerate, + require_bitsandbytes_version_greater, + require_peft_backend, + require_torch, + require_torch_accelerator, + require_torch_version_greater, + require_transformers_version_greater, + slow, + torch_device, +) +from ..test_torch_compile_utils import QuantCompileTests + + +def get_some_linear_layer(model): + if model.__class__.__name__ in ["SD3Transformer2DModel", "FluxTransformer2DModel"]: + return model.transformer_blocks[0].attn.to_q + else: + return NotImplementedError("Don't know what layer to retrieve here.") + + +if is_transformers_available(): + from transformers import BitsAndBytesConfig as BnbConfig + from transformers import T5EncoderModel + +if is_torch_available(): + import torch + + from ..utils import LoRALayer, get_memory_consumption_stat + + +if is_bitsandbytes_available(): + import bitsandbytes as bnb + + from diffusers.quantizers.bitsandbytes.utils import replace_with_bnb_linear + + +@require_bitsandbytes_version_greater("0.43.2") +@require_accelerate +@require_torch +@require_torch_accelerator +@slow +class Base4bitTests(unittest.TestCase): + # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected) + # Therefore here we use only SD3 to test our module + model_name = "stabilityai/stable-diffusion-3-medium-diffusers" + + # This was obtained on audace so the number might slightly change + expected_rel_difference = 3.69 + + expected_memory_saving_ratio = 0.8 + + prompt = "a beautiful sunset amidst the mountains." + num_inference_steps = 10 + seed = 0 + + @classmethod + def setUpClass(cls): + cls.is_deterministic_enabled = torch.are_deterministic_algorithms_enabled() + if not cls.is_deterministic_enabled: + torch.use_deterministic_algorithms(True) + + @classmethod + def tearDownClass(cls): + if not cls.is_deterministic_enabled: + torch.use_deterministic_algorithms(False) + + def get_dummy_inputs(self): + prompt_embeds = load_pt( + "https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/prompt_embeds.pt", + torch_device, + ) + pooled_prompt_embeds = load_pt( + "https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/pooled_prompt_embeds.pt", + torch_device, + ) + latent_model_input = load_pt( + "https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/latent_model_input.pt", + torch_device, + ) + + input_dict_for_transformer = { + "hidden_states": latent_model_input, + "encoder_hidden_states": prompt_embeds, + "pooled_projections": pooled_prompt_embeds, + "timestep": torch.Tensor([1.0]), + "return_dict": False, + } + return input_dict_for_transformer + + +class BnB4BitBasicTests(Base4bitTests): + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + # Models + self.model_fp16 = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", torch_dtype=torch.float16 + ) + nf4_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + ) + self.model_4bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=nf4_config, device_map=torch_device + ) + + def tearDown(self): + if hasattr(self, "model_fp16"): + del self.model_fp16 + if hasattr(self, "model_4bit"): + del self.model_4bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_quantization_num_parameters(self): + r""" + Test if the number of returned parameters is correct + """ + num_params_4bit = self.model_4bit.num_parameters() + num_params_fp16 = self.model_fp16.num_parameters() + + self.assertEqual(num_params_4bit, num_params_fp16) + + def test_quantization_config_json_serialization(self): + r""" + A simple test to check if the quantization config is correctly serialized and deserialized + """ + config = self.model_4bit.config + + self.assertTrue("quantization_config" in config) + + _ = config["quantization_config"].to_dict() + _ = config["quantization_config"].to_diff_dict() + + _ = config["quantization_config"].to_json_string() + + def test_memory_footprint(self): + r""" + A simple test to check if the model conversion has been done correctly by checking on the + memory footprint of the converted model and the class type of the linear layers of the converted models + """ + mem_fp16 = self.model_fp16.get_memory_footprint() + mem_4bit = self.model_4bit.get_memory_footprint() + + self.assertAlmostEqual(mem_fp16 / mem_4bit, self.expected_rel_difference, delta=1e-2) + linear = get_some_linear_layer(self.model_4bit) + self.assertTrue(linear.weight.__class__ == bnb.nn.Params4bit) + + def test_model_memory_usage(self): + # Delete to not let anything interfere. + del self.model_4bit, self.model_fp16 + + # Re-instantiate. + inputs = self.get_dummy_inputs() + inputs = { + k: v.to(device=torch_device, dtype=torch.float16) for k, v in inputs.items() if not isinstance(v, bool) + } + model_fp16 = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", torch_dtype=torch.float16 + ).to(torch_device) + unquantized_model_memory = get_memory_consumption_stat(model_fp16, inputs) + del model_fp16 + + nf4_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + ) + model_4bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=nf4_config, torch_dtype=torch.float16 + ) + quantized_model_memory = get_memory_consumption_stat(model_4bit, inputs) + assert unquantized_model_memory / quantized_model_memory >= self.expected_memory_saving_ratio + + def test_original_dtype(self): + r""" + A simple test to check if the model successfully stores the original dtype + """ + self.assertTrue("_pre_quantization_dtype" in self.model_4bit.config) + self.assertFalse("_pre_quantization_dtype" in self.model_fp16.config) + self.assertTrue(self.model_4bit.config["_pre_quantization_dtype"] == torch.float16) + + def test_keep_modules_in_fp32(self): + r""" + A simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32. + Also ensures if inference works. + """ + fp32_modules = SD3Transformer2DModel._keep_in_fp32_modules + SD3Transformer2DModel._keep_in_fp32_modules = ["proj_out"] + + nf4_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + ) + model = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=nf4_config, device_map=torch_device + ) + + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Linear): + if name in model._keep_in_fp32_modules: + self.assertTrue(module.weight.dtype == torch.float32) + else: + # 4-bit parameters are packed in uint8 variables + self.assertTrue(module.weight.dtype == torch.uint8) + + # test if inference works. + with torch.no_grad() and torch.amp.autocast(torch_device, dtype=torch.float16): + input_dict_for_transformer = self.get_dummy_inputs() + model_inputs = { + k: v.to(device=torch_device) for k, v in input_dict_for_transformer.items() if not isinstance(v, bool) + } + model_inputs.update({k: v for k, v in input_dict_for_transformer.items() if k not in model_inputs}) + _ = model(**model_inputs) + + SD3Transformer2DModel._keep_in_fp32_modules = fp32_modules + + def test_linear_are_4bit(self): + r""" + A simple test to check if the model conversion has been done correctly by checking on the + memory footprint of the converted model and the class type of the linear layers of the converted models + """ + self.model_fp16.get_memory_footprint() + self.model_4bit.get_memory_footprint() + + for name, module in self.model_4bit.named_modules(): + if isinstance(module, torch.nn.Linear): + if name not in ["proj_out"]: + # 4-bit parameters are packed in uint8 variables + self.assertTrue(module.weight.dtype == torch.uint8) + + def test_config_from_pretrained(self): + transformer_4bit = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/flux.1-dev-nf4-pkg", subfolder="transformer" + ) + linear = get_some_linear_layer(transformer_4bit) + self.assertTrue(linear.weight.__class__ == bnb.nn.Params4bit) + self.assertTrue(hasattr(linear.weight, "quant_state")) + self.assertTrue(linear.weight.quant_state.__class__ == bnb.functional.QuantState) + + def test_device_assignment(self): + mem_before = self.model_4bit.get_memory_footprint() + + # Move to CPU + self.model_4bit.to("cpu") + self.assertEqual(self.model_4bit.device.type, "cpu") + self.assertAlmostEqual(self.model_4bit.get_memory_footprint(), mem_before) + + # Move back to CUDA device + for device in [0, f"{torch_device}", f"{torch_device}:0", "call()"]: + if device == "call()": + self.model_4bit.to(f"{torch_device}:0") + else: + self.model_4bit.to(device) + self.assertEqual(self.model_4bit.device, torch.device(0)) + self.assertAlmostEqual(self.model_4bit.get_memory_footprint(), mem_before) + self.model_4bit.to("cpu") + + def test_device_and_dtype_assignment(self): + r""" + Test whether trying to cast (or assigning a device to) a model after converting it in 4-bit will throw an error. + Checks also if other models are casted correctly. Device placement, however, is supported. + """ + with self.assertRaises(ValueError): + # Tries with a `dtype` + self.model_4bit.to(torch.float16) + + with self.assertRaises(ValueError): + # Tries with a `device` and `dtype` + self.model_4bit.to(device=f"{torch_device}:0", dtype=torch.float16) + + with self.assertRaises(ValueError): + # Tries with a cast + self.model_4bit.float() + + with self.assertRaises(ValueError): + # Tries with a cast + self.model_4bit.half() + + # This should work + self.model_4bit.to(torch_device) + + # Test if we did not break anything + self.model_fp16 = self.model_fp16.to(dtype=torch.float32, device=torch_device) + input_dict_for_transformer = self.get_dummy_inputs() + model_inputs = { + k: v.to(dtype=torch.float32, device=torch_device) + for k, v in input_dict_for_transformer.items() + if not isinstance(v, bool) + } + model_inputs.update({k: v for k, v in input_dict_for_transformer.items() if k not in model_inputs}) + with torch.no_grad(): + _ = self.model_fp16(**model_inputs) + + # Check this does not throw an error + _ = self.model_fp16.to("cpu") + + # Check this does not throw an error + _ = self.model_fp16.half() + + # Check this does not throw an error + _ = self.model_fp16.float() + + # Check that this does not throw an error + _ = self.model_fp16.to(torch_device) + + def test_bnb_4bit_wrong_config(self): + r""" + Test whether creating a bnb config with unsupported values leads to errors. + """ + with self.assertRaises(ValueError): + _ = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_storage="add") + + def test_bnb_4bit_errors_loading_incorrect_state_dict(self): + r""" + Test if loading with an incorrect state dict raises an error. + """ + with tempfile.TemporaryDirectory() as tmpdirname: + nf4_config = BitsAndBytesConfig(load_in_4bit=True) + model_4bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=nf4_config, device_map=torch_device + ) + model_4bit.save_pretrained(tmpdirname) + del model_4bit + + with self.assertRaises(ValueError) as err_context: + state_dict = safetensors.torch.load_file( + os.path.join(tmpdirname, "diffusion_pytorch_model.safetensors") + ) + + # corrupt the state dict + key_to_target = "context_embedder.weight" # can be other keys too. + compatible_param = state_dict[key_to_target] + corrupted_param = torch.randn(compatible_param.shape[0] - 1, 1) + state_dict[key_to_target] = bnb.nn.Params4bit(corrupted_param, requires_grad=False) + safetensors.torch.save_file( + state_dict, os.path.join(tmpdirname, "diffusion_pytorch_model.safetensors") + ) + + _ = SD3Transformer2DModel.from_pretrained(tmpdirname) + + assert key_to_target in str(err_context.exception) + + def test_bnb_4bit_logs_warning_for_no_quantization(self): + model_with_no_linear = torch.nn.Sequential(torch.nn.Conv2d(4, 4, 3), torch.nn.ReLU()) + quantization_config = BitsAndBytesConfig(load_in_4bit=True) + logger = logging.get_logger("diffusers.quantizers.bitsandbytes.utils") + logger.setLevel(30) + with CaptureLogger(logger) as cap_logger: + _ = replace_with_bnb_linear(model_with_no_linear, quantization_config=quantization_config) + assert ( + "You are loading your model in 8bit or 4bit but no linear modules were found in your model." + in cap_logger.out + ) + + +class BnB4BitTrainingTests(Base4bitTests): + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + nf4_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + ) + self.model_4bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=nf4_config, device_map=torch_device + ) + + def test_training(self): + # Step 1: freeze all parameters + for param in self.model_4bit.parameters(): + param.requires_grad = False # freeze the model - train adapters later + if param.ndim == 1: + # cast the small parameters (e.g. layernorm) to fp32 for stability + param.data = param.data.to(torch.float32) + + # Step 2: add adapters + for _, module in self.model_4bit.named_modules(): + if "Attention" in repr(type(module)): + module.to_k = LoRALayer(module.to_k, rank=4) + module.to_q = LoRALayer(module.to_q, rank=4) + module.to_v = LoRALayer(module.to_v, rank=4) + + # Step 3: dummy batch + input_dict_for_transformer = self.get_dummy_inputs() + model_inputs = { + k: v.to(device=torch_device) for k, v in input_dict_for_transformer.items() if not isinstance(v, bool) + } + model_inputs.update({k: v for k, v in input_dict_for_transformer.items() if k not in model_inputs}) + + # Step 4: Check if the gradient is not None + with torch.amp.autocast(torch_device, dtype=torch.float16): + out = self.model_4bit(**model_inputs)[0] + out.norm().backward() + + for module in self.model_4bit.modules(): + if isinstance(module, LoRALayer): + self.assertTrue(module.adapter[1].weight.grad is not None) + self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0) + + +@require_transformers_version_greater("4.44.0") +class SlowBnb4BitTests(Base4bitTests): + def setUp(self) -> None: + gc.collect() + backend_empty_cache(torch_device) + + nf4_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + ) + model_4bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=nf4_config, device_map=torch_device + ) + self.pipeline_4bit = DiffusionPipeline.from_pretrained( + self.model_name, transformer=model_4bit, torch_dtype=torch.float16 + ) + self.pipeline_4bit.enable_model_cpu_offload() + + def tearDown(self): + del self.pipeline_4bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_quality(self): + output = self.pipeline_4bit( + prompt=self.prompt, + num_inference_steps=self.num_inference_steps, + generator=torch.manual_seed(self.seed), + output_type="np", + ).images + + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.1123, 0.1296, 0.1609, 0.1042, 0.1230, 0.1274, 0.0928, 0.1165, 0.1216]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-2) + + def test_generate_quality_dequantize(self): + r""" + Test that loading the model and unquantize it produce correct results. + """ + self.pipeline_4bit.transformer.dequantize() + output = self.pipeline_4bit( + prompt=self.prompt, + num_inference_steps=self.num_inference_steps, + generator=torch.manual_seed(self.seed), + output_type="np", + ).images + + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.1216, 0.1387, 0.1584, 0.1152, 0.1318, 0.1282, 0.1062, 0.1226, 0.1228]) + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-3) + + # Since we offloaded the `pipeline_4bit.transformer` to CPU (result of `enable_model_cpu_offload()), check + # the following. + self.assertTrue(self.pipeline_4bit.transformer.device.type == "cpu") + # calling it again shouldn't be a problem + _ = self.pipeline_4bit( + prompt=self.prompt, + num_inference_steps=2, + generator=torch.manual_seed(self.seed), + output_type="np", + ).images + + def test_moving_to_cpu_throws_warning(self): + nf4_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + ) + model_4bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=nf4_config, device_map=torch_device + ) + + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + logger.setLevel(30) + with CaptureLogger(logger) as cap_logger: + # Because `model.dtype` will return torch.float16 as SD3 transformer has + # a conv layer as the first layer. + _ = DiffusionPipeline.from_pretrained( + self.model_name, transformer=model_4bit, torch_dtype=torch.float16 + ).to("cpu") + + assert "Pipelines loaded with `dtype=torch.float16`" in cap_logger.out + + @pytest.mark.xfail( + condition=is_accelerate_version("<=", "1.1.1"), + reason="Test will pass after https://github.com/huggingface/accelerate/pull/3223 is in a release.", + strict=True, + ) + def test_pipeline_cuda_placement_works_with_nf4(self): + transformer_nf4_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + ) + transformer_4bit = SD3Transformer2DModel.from_pretrained( + self.model_name, + subfolder="transformer", + quantization_config=transformer_nf4_config, + torch_dtype=torch.float16, + device_map=torch_device, + ) + text_encoder_3_nf4_config = BnbConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.float16, + ) + text_encoder_3_4bit = T5EncoderModel.from_pretrained( + self.model_name, + subfolder="text_encoder_3", + quantization_config=text_encoder_3_nf4_config, + torch_dtype=torch.float16, + device_map=torch_device, + ) + # CUDA device placement works. + pipeline_4bit = DiffusionPipeline.from_pretrained( + self.model_name, + transformer=transformer_4bit, + text_encoder_3=text_encoder_3_4bit, + torch_dtype=torch.float16, + ).to(torch_device) + + # Check if inference works. + _ = pipeline_4bit(self.prompt, max_sequence_length=20, num_inference_steps=2) + + del pipeline_4bit + + def test_device_map(self): + """ + Test if the quantized model is working properly with "auto". + cpu/disk offloading as well doesn't work with bnb. + """ + + def get_dummy_tensor_inputs(device=None, seed: int = 0): + batch_size = 1 + num_latent_channels = 4 + num_image_channels = 3 + height = width = 4 + sequence_length = 48 + embedding_dim = 32 + + torch.manual_seed(seed) + hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to( + device, dtype=torch.bfloat16 + ) + torch.manual_seed(seed) + encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to( + device, dtype=torch.bfloat16 + ) + + torch.manual_seed(seed) + pooled_prompt_embeds = torch.randn((batch_size, embedding_dim)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + text_ids = torch.randn((sequence_length, num_image_channels)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + image_ids = torch.randn((height * width, num_image_channels)).to(device, dtype=torch.bfloat16) + + timestep = torch.tensor([1.0]).to(device, dtype=torch.bfloat16).expand(batch_size) + + return { + "hidden_states": hidden_states, + "encoder_hidden_states": encoder_hidden_states, + "pooled_projections": pooled_prompt_embeds, + "txt_ids": text_ids, + "img_ids": image_ids, + "timestep": timestep, + } + + inputs = get_dummy_tensor_inputs(torch_device) + expected_slice = np.array( + [0.47070312, 0.00390625, -0.03662109, -0.19628906, -0.53125, 0.5234375, -0.17089844, -0.59375, 0.578125] + ) + + # non sharded + quantization_config = BitsAndBytesConfig( + load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16 + ) + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + subfolder="transformer", + quantization_config=quantization_config, + device_map="auto", + torch_dtype=torch.bfloat16, + ) + + weight = quantized_model.transformer_blocks[0].ff.net[2].weight + self.assertTrue(isinstance(weight, bnb.nn.modules.Params4bit)) + + output = quantized_model(**inputs)[0] + output_slice = output.flatten()[-9:].detach().float().cpu().numpy() + self.assertTrue(numpy_cosine_similarity_distance(output_slice, expected_slice) < 1e-3) + + # sharded + + quantization_config = BitsAndBytesConfig( + load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16 + ) + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-sharded", + subfolder="transformer", + quantization_config=quantization_config, + device_map="auto", + torch_dtype=torch.bfloat16, + ) + + weight = quantized_model.transformer_blocks[0].ff.net[2].weight + self.assertTrue(isinstance(weight, bnb.nn.modules.Params4bit)) + + output = quantized_model(**inputs)[0] + output_slice = output.flatten()[-9:].detach().float().cpu().numpy() + + self.assertTrue(numpy_cosine_similarity_distance(output_slice, expected_slice) < 1e-3) + + +@require_transformers_version_greater("4.44.0") +class SlowBnb4BitFluxTests(Base4bitTests): + def setUp(self) -> None: + gc.collect() + backend_empty_cache(torch_device) + + model_id = "hf-internal-testing/flux.1-dev-nf4-pkg" + t5_4bit = T5EncoderModel.from_pretrained(model_id, subfolder="text_encoder_2") + transformer_4bit = FluxTransformer2DModel.from_pretrained(model_id, subfolder="transformer") + self.pipeline_4bit = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + text_encoder_2=t5_4bit, + transformer=transformer_4bit, + torch_dtype=torch.float16, + ) + self.pipeline_4bit.enable_model_cpu_offload() + + def tearDown(self): + del self.pipeline_4bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_quality(self): + # keep the resolution and max tokens to a lower number for faster execution. + output = self.pipeline_4bit( + prompt=self.prompt, + num_inference_steps=self.num_inference_steps, + generator=torch.manual_seed(self.seed), + height=256, + width=256, + max_sequence_length=64, + output_type="np", + ).images + + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.0583, 0.0586, 0.0632, 0.0815, 0.0813, 0.0947, 0.1040, 0.1145, 0.1265]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-3) + + @require_peft_backend + def test_lora_loading(self): + self.pipeline_4bit.load_lora_weights( + hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd" + ) + self.pipeline_4bit.set_adapters("hyper-sd", adapter_weights=0.125) + + output = self.pipeline_4bit( + prompt=self.prompt, + height=256, + width=256, + max_sequence_length=64, + output_type="np", + num_inference_steps=8, + generator=torch.Generator().manual_seed(42), + ).images + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.5347, 0.5342, 0.5283, 0.5093, 0.4988, 0.5093, 0.5044, 0.5015, 0.4946]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-3) + + +@require_transformers_version_greater("4.44.0") +@require_peft_backend +class SlowBnb4BitFluxControlWithLoraTests(Base4bitTests): + def setUp(self) -> None: + gc.collect() + backend_empty_cache(torch_device) + + self.pipeline_4bit = FluxControlPipeline.from_pretrained("eramth/flux-4bit", torch_dtype=torch.float16) + self.pipeline_4bit.enable_model_cpu_offload() + + def tearDown(self): + del self.pipeline_4bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_lora_loading(self): + self.pipeline_4bit.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora") + + output = self.pipeline_4bit( + prompt=self.prompt, + control_image=Image.new(mode="RGB", size=(256, 256)), + height=256, + width=256, + max_sequence_length=64, + output_type="np", + num_inference_steps=8, + generator=torch.Generator().manual_seed(42), + ).images + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.1636, 0.1675, 0.1982, 0.1743, 0.1809, 0.1936, 0.1743, 0.2095, 0.2139]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-3, msg=f"{out_slice=} != {expected_slice=}") + + +@slow +class BaseBnb4BitSerializationTests(Base4bitTests): + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def test_serialization(self, quant_type="nf4", double_quant=True, safe_serialization=True): + r""" + Test whether it is possible to serialize a model in 4-bit. Uses most typical params as default. + See ExtendedSerializationTest class for more params combinations. + """ + + self.quantization_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type=quant_type, + bnb_4bit_use_double_quant=double_quant, + bnb_4bit_compute_dtype=torch.bfloat16, + ) + model_0 = SD3Transformer2DModel.from_pretrained( + self.model_name, + subfolder="transformer", + quantization_config=self.quantization_config, + device_map=torch_device, + ) + self.assertTrue("_pre_quantization_dtype" in model_0.config) + with tempfile.TemporaryDirectory() as tmpdirname: + model_0.save_pretrained(tmpdirname, safe_serialization=safe_serialization) + + config = SD3Transformer2DModel.load_config(tmpdirname) + self.assertTrue("quantization_config" in config) + self.assertTrue("_pre_quantization_dtype" not in config) + + model_1 = SD3Transformer2DModel.from_pretrained(tmpdirname) + + # checking quantized linear module weight + linear = get_some_linear_layer(model_1) + self.assertTrue(linear.weight.__class__ == bnb.nn.Params4bit) + self.assertTrue(hasattr(linear.weight, "quant_state")) + self.assertTrue(linear.weight.quant_state.__class__ == bnb.functional.QuantState) + + # checking memory footpring + self.assertAlmostEqual(model_0.get_memory_footprint() / model_1.get_memory_footprint(), 1, places=2) + + # Matching all parameters and their quant_state items: + d0 = dict(model_0.named_parameters()) + d1 = dict(model_1.named_parameters()) + self.assertTrue(d0.keys() == d1.keys()) + + for k in d0.keys(): + self.assertTrue(d0[k].shape == d1[k].shape) + self.assertTrue(d0[k].device.type == d1[k].device.type) + self.assertTrue(d0[k].device == d1[k].device) + self.assertTrue(d0[k].dtype == d1[k].dtype) + self.assertTrue(torch.equal(d0[k], d1[k].to(d0[k].device))) + + if isinstance(d0[k], bnb.nn.modules.Params4bit): + for v0, v1 in zip( + d0[k].quant_state.as_dict().values(), + d1[k].quant_state.as_dict().values(), + ): + if isinstance(v0, torch.Tensor): + self.assertTrue(torch.equal(v0, v1.to(v0.device))) + else: + self.assertTrue(v0 == v1) + + # comparing forward() outputs + dummy_inputs = self.get_dummy_inputs() + inputs = {k: v.to(torch_device) for k, v in dummy_inputs.items() if isinstance(v, torch.Tensor)} + inputs.update({k: v for k, v in dummy_inputs.items() if k not in inputs}) + out_0 = model_0(**inputs)[0] + out_1 = model_1(**inputs)[0] + self.assertTrue(torch.equal(out_0, out_1)) + + +class ExtendedSerializationTest(BaseBnb4BitSerializationTests): + """ + tests more combinations of parameters + """ + + def test_nf4_single_unsafe(self): + self.test_serialization(quant_type="nf4", double_quant=False, safe_serialization=False) + + def test_nf4_single_safe(self): + self.test_serialization(quant_type="nf4", double_quant=False, safe_serialization=True) + + def test_nf4_double_unsafe(self): + self.test_serialization(quant_type="nf4", double_quant=True, safe_serialization=False) + + # nf4 double safetensors quantization is tested in test_serialization() method from the parent class + + def test_fp4_single_unsafe(self): + self.test_serialization(quant_type="fp4", double_quant=False, safe_serialization=False) + + def test_fp4_single_safe(self): + self.test_serialization(quant_type="fp4", double_quant=False, safe_serialization=True) + + def test_fp4_double_unsafe(self): + self.test_serialization(quant_type="fp4", double_quant=True, safe_serialization=False) + + def test_fp4_double_safe(self): + self.test_serialization(quant_type="fp4", double_quant=True, safe_serialization=True) + + +@require_torch_version_greater("2.7.1") +@require_bitsandbytes_version_greater("0.45.5") +class Bnb4BitCompileTests(QuantCompileTests, unittest.TestCase): + @property + def quantization_config(self): + return PipelineQuantizationConfig( + quant_backend="bitsandbytes_4bit", + quant_kwargs={ + "load_in_4bit": True, + "bnb_4bit_quant_type": "nf4", + "bnb_4bit_compute_dtype": torch.bfloat16, + }, + components_to_quantize=["transformer", "text_encoder_2"], + ) + + @require_bitsandbytes_version_greater("0.46.1") + def test_torch_compile(self): + torch._dynamo.config.capture_dynamic_output_shape_ops = True + super().test_torch_compile() + + def test_torch_compile_with_group_offload_leaf(self): + super()._test_torch_compile_with_group_offload_leaf(use_stream=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/test_mixed_int8.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/test_mixed_int8.py new file mode 100644 index 0000000000000000000000000000000000000000..031fdc9f9e276d134d228f1bfc7f1bdfdfb19391 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/bnb/test_mixed_int8.py @@ -0,0 +1,860 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a clone of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import gc +import tempfile +import unittest + +import numpy as np +import pytest +from huggingface_hub import hf_hub_download +from PIL import Image + +from diffusers import ( + BitsAndBytesConfig, + DiffusionPipeline, + FluxControlPipeline, + FluxTransformer2DModel, + SanaTransformer2DModel, + SD3Transformer2DModel, + logging, +) +from diffusers.quantizers import PipelineQuantizationConfig +from diffusers.utils import is_accelerate_version + +from ...testing_utils import ( + CaptureLogger, + backend_empty_cache, + is_bitsandbytes_available, + is_torch_available, + is_transformers_available, + load_pt, + numpy_cosine_similarity_distance, + require_accelerate, + require_bitsandbytes_version_greater, + require_peft_backend, + require_peft_version_greater, + require_torch, + require_torch_accelerator, + require_torch_version_greater_equal, + require_transformers_version_greater, + slow, + torch_device, +) +from ..test_torch_compile_utils import QuantCompileTests + + +def get_some_linear_layer(model): + if model.__class__.__name__ in ["SD3Transformer2DModel", "FluxTransformer2DModel"]: + return model.transformer_blocks[0].attn.to_q + else: + return NotImplementedError("Don't know what layer to retrieve here.") + + +if is_transformers_available(): + from transformers import BitsAndBytesConfig as BnbConfig + from transformers import T5EncoderModel + +if is_torch_available(): + import torch + + from ..utils import LoRALayer, get_memory_consumption_stat + + +if is_bitsandbytes_available(): + import bitsandbytes as bnb + + from diffusers.quantizers.bitsandbytes import replace_with_bnb_linear + + +@require_bitsandbytes_version_greater("0.43.2") +@require_accelerate +@require_torch +@require_torch_accelerator +@slow +class Base8bitTests(unittest.TestCase): + # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected) + # Therefore here we use only SD3 to test our module + model_name = "stabilityai/stable-diffusion-3-medium-diffusers" + + # This was obtained on audace so the number might slightly change + expected_rel_difference = 1.94 + + expected_memory_saving_ratio = 0.7 + + prompt = "a beautiful sunset amidst the mountains." + num_inference_steps = 10 + seed = 0 + + @classmethod + def setUpClass(cls): + cls.is_deterministic_enabled = torch.are_deterministic_algorithms_enabled() + if not cls.is_deterministic_enabled: + torch.use_deterministic_algorithms(True) + + @classmethod + def tearDownClass(cls): + if not cls.is_deterministic_enabled: + torch.use_deterministic_algorithms(False) + + def get_dummy_inputs(self): + prompt_embeds = load_pt( + "https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/prompt_embeds.pt", + map_location="cpu", + ) + pooled_prompt_embeds = load_pt( + "https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/pooled_prompt_embeds.pt", + map_location="cpu", + ) + latent_model_input = load_pt( + "https://huggingface.co/datasets/hf-internal-testing/bnb-diffusers-testing-artifacts/resolve/main/latent_model_input.pt", + map_location="cpu", + ) + + input_dict_for_transformer = { + "hidden_states": latent_model_input, + "encoder_hidden_states": prompt_embeds, + "pooled_projections": pooled_prompt_embeds, + "timestep": torch.Tensor([1.0]), + "return_dict": False, + } + return input_dict_for_transformer + + +class BnB8bitBasicTests(Base8bitTests): + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + # Models + self.model_fp16 = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", torch_dtype=torch.float16 + ) + mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True) + self.model_8bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=mixed_int8_config, device_map=torch_device + ) + + def tearDown(self): + if hasattr(self, "model_fp16"): + del self.model_fp16 + if hasattr(self, "model_8bit"): + del self.model_8bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_quantization_num_parameters(self): + r""" + Test if the number of returned parameters is correct + """ + num_params_8bit = self.model_8bit.num_parameters() + num_params_fp16 = self.model_fp16.num_parameters() + + self.assertEqual(num_params_8bit, num_params_fp16) + + def test_quantization_config_json_serialization(self): + r""" + A simple test to check if the quantization config is correctly serialized and deserialized + """ + config = self.model_8bit.config + + self.assertTrue("quantization_config" in config) + + _ = config["quantization_config"].to_dict() + _ = config["quantization_config"].to_diff_dict() + + _ = config["quantization_config"].to_json_string() + + def test_memory_footprint(self): + r""" + A simple test to check if the model conversion has been done correctly by checking on the + memory footprint of the converted model and the class type of the linear layers of the converted models + """ + mem_fp16 = self.model_fp16.get_memory_footprint() + mem_8bit = self.model_8bit.get_memory_footprint() + + self.assertAlmostEqual(mem_fp16 / mem_8bit, self.expected_rel_difference, delta=1e-2) + linear = get_some_linear_layer(self.model_8bit) + self.assertTrue(linear.weight.__class__ == bnb.nn.Int8Params) + + def test_model_memory_usage(self): + # Delete to not let anything interfere. + del self.model_8bit, self.model_fp16 + + # Re-instantiate. + inputs = self.get_dummy_inputs() + inputs = { + k: v.to(device=torch_device, dtype=torch.float16) for k, v in inputs.items() if not isinstance(v, bool) + } + model_fp16 = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", torch_dtype=torch.float16 + ).to(torch_device) + unquantized_model_memory = get_memory_consumption_stat(model_fp16, inputs) + del model_fp16 + + config = BitsAndBytesConfig(load_in_8bit=True) + model_8bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=config, torch_dtype=torch.float16 + ) + quantized_model_memory = get_memory_consumption_stat(model_8bit, inputs) + assert unquantized_model_memory / quantized_model_memory >= self.expected_memory_saving_ratio + + def test_original_dtype(self): + r""" + A simple test to check if the model successfully stores the original dtype + """ + self.assertTrue("_pre_quantization_dtype" in self.model_8bit.config) + self.assertFalse("_pre_quantization_dtype" in self.model_fp16.config) + self.assertTrue(self.model_8bit.config["_pre_quantization_dtype"] == torch.float16) + + def test_keep_modules_in_fp32(self): + r""" + A simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32. + Also ensures if inference works. + """ + fp32_modules = SD3Transformer2DModel._keep_in_fp32_modules + SD3Transformer2DModel._keep_in_fp32_modules = ["proj_out"] + + mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True) + model = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=mixed_int8_config, device_map=torch_device + ) + + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Linear): + if name in model._keep_in_fp32_modules: + self.assertTrue(module.weight.dtype == torch.float32) + else: + # 8-bit parameters are packed in int8 variables + self.assertTrue(module.weight.dtype == torch.int8) + + # test if inference works. + with torch.no_grad() and torch.autocast(model.device.type, dtype=torch.float16): + input_dict_for_transformer = self.get_dummy_inputs() + model_inputs = { + k: v.to(device=torch_device) for k, v in input_dict_for_transformer.items() if not isinstance(v, bool) + } + model_inputs.update({k: v for k, v in input_dict_for_transformer.items() if k not in model_inputs}) + _ = model(**model_inputs) + + SD3Transformer2DModel._keep_in_fp32_modules = fp32_modules + + def test_linear_are_8bit(self): + r""" + A simple test to check if the model conversion has been done correctly by checking on the + memory footprint of the converted model and the class type of the linear layers of the converted models + """ + self.model_fp16.get_memory_footprint() + self.model_8bit.get_memory_footprint() + + for name, module in self.model_8bit.named_modules(): + if isinstance(module, torch.nn.Linear): + if name not in ["proj_out"]: + # 8-bit parameters are packed in int8 variables + self.assertTrue(module.weight.dtype == torch.int8) + + def test_llm_skip(self): + r""" + A simple test to check if `llm_int8_skip_modules` works as expected + """ + config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_skip_modules=["proj_out"]) + model_8bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=config, device_map=torch_device + ) + linear = get_some_linear_layer(model_8bit) + self.assertTrue(linear.weight.dtype == torch.int8) + self.assertTrue(isinstance(linear, bnb.nn.Linear8bitLt)) + + self.assertTrue(isinstance(model_8bit.proj_out, torch.nn.Linear)) + self.assertTrue(model_8bit.proj_out.weight.dtype != torch.int8) + + def test_config_from_pretrained(self): + transformer_8bit = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/flux.1-dev-int8-pkg", subfolder="transformer" + ) + linear = get_some_linear_layer(transformer_8bit) + self.assertTrue(linear.weight.__class__ == bnb.nn.Int8Params) + self.assertTrue(hasattr(linear.weight, "SCB")) + + @require_bitsandbytes_version_greater("0.48.0") + def test_device_and_dtype_assignment(self): + r""" + Test whether trying to cast (or assigning a device to) a model after converting it in 8-bit will throw an error. + Checks also if other models are casted correctly. + """ + + with self.assertRaises(ValueError): + # Tries with a `dtype`` + self.model_8bit.to(torch.float16) + + with self.assertRaises(ValueError): + # Tries with a `device` + self.model_8bit.float() + + with self.assertRaises(ValueError): + # Tries with a `dtype` + self.model_8bit.half() + + # This should work with 0.48.0 + self.model_8bit.to("cpu") + self.model_8bit.to(torch.device(f"{torch_device}:0")) + + # Test if we did not break anything + self.model_fp16 = self.model_fp16.to(dtype=torch.float32, device=torch_device) + input_dict_for_transformer = self.get_dummy_inputs() + model_inputs = { + k: v.to(dtype=torch.float32, device=torch_device) + for k, v in input_dict_for_transformer.items() + if not isinstance(v, bool) + } + model_inputs.update({k: v for k, v in input_dict_for_transformer.items() if k not in model_inputs}) + with torch.no_grad(): + _ = self.model_fp16(**model_inputs) + + # Check this does not throw an error + _ = self.model_fp16.to("cpu") + + # Check this does not throw an error + _ = self.model_fp16.half() + + # Check this does not throw an error + _ = self.model_fp16.float() + + # Check that this does not throw an error + _ = self.model_fp16.to(torch_device) + + def test_bnb_8bit_logs_warning_for_no_quantization(self): + model_with_no_linear = torch.nn.Sequential(torch.nn.Conv2d(4, 4, 3), torch.nn.ReLU()) + quantization_config = BitsAndBytesConfig(load_in_8bit=True) + logger = logging.get_logger("diffusers.quantizers.bitsandbytes.utils") + logger.setLevel(30) + with CaptureLogger(logger) as cap_logger: + _ = replace_with_bnb_linear(model_with_no_linear, quantization_config=quantization_config) + assert ( + "You are loading your model in 8bit or 4bit but no linear modules were found in your model." + in cap_logger.out + ) + + +class Bnb8bitDeviceTests(Base8bitTests): + def setUp(self) -> None: + gc.collect() + backend_empty_cache(torch_device) + + mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True) + self.model_8bit = SanaTransformer2DModel.from_pretrained( + "Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers", + subfolder="transformer", + quantization_config=mixed_int8_config, + device_map=torch_device, + ) + + def tearDown(self): + del self.model_8bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_buffers_device_assignment(self): + for buffer_name, buffer in self.model_8bit.named_buffers(): + self.assertEqual( + buffer.device.type, + torch.device(torch_device).type, + f"Expected device {torch_device} for {buffer_name} got {buffer.device}.", + ) + + +class BnB8bitTrainingTests(Base8bitTests): + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True) + self.model_8bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=mixed_int8_config, device_map=torch_device + ) + + def test_training(self): + # Step 1: freeze all parameters + for param in self.model_8bit.parameters(): + param.requires_grad = False # freeze the model - train adapters later + if param.ndim == 1: + # cast the small parameters (e.g. layernorm) to fp32 for stability + param.data = param.data.to(torch.float32) + + # Step 2: add adapters + for _, module in self.model_8bit.named_modules(): + if "Attention" in repr(type(module)): + module.to_k = LoRALayer(module.to_k, rank=4) + module.to_q = LoRALayer(module.to_q, rank=4) + module.to_v = LoRALayer(module.to_v, rank=4) + + # Step 3: dummy batch + input_dict_for_transformer = self.get_dummy_inputs() + model_inputs = { + k: v.to(device=torch_device) for k, v in input_dict_for_transformer.items() if not isinstance(v, bool) + } + model_inputs.update({k: v for k, v in input_dict_for_transformer.items() if k not in model_inputs}) + + # Step 4: Check if the gradient is not None + with torch.amp.autocast(torch_device, dtype=torch.float16): + out = self.model_8bit(**model_inputs)[0] + out.norm().backward() + + for module in self.model_8bit.modules(): + if isinstance(module, LoRALayer): + self.assertTrue(module.adapter[1].weight.grad is not None) + self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0) + + +@require_transformers_version_greater("4.44.0") +class SlowBnb8bitTests(Base8bitTests): + def setUp(self) -> None: + gc.collect() + backend_empty_cache(torch_device) + + mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True) + model_8bit = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=mixed_int8_config, device_map=torch_device + ) + self.pipeline_8bit = DiffusionPipeline.from_pretrained( + self.model_name, transformer=model_8bit, torch_dtype=torch.float16 + ) + self.pipeline_8bit.enable_model_cpu_offload() + + def tearDown(self): + del self.pipeline_8bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_quality(self): + output = self.pipeline_8bit( + prompt=self.prompt, + num_inference_steps=self.num_inference_steps, + generator=torch.manual_seed(self.seed), + output_type="np", + ).images + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.0674, 0.0623, 0.0364, 0.0632, 0.0671, 0.0430, 0.0317, 0.0493, 0.0583]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-2) + + def test_model_cpu_offload_raises_warning(self): + model_8bit = SD3Transformer2DModel.from_pretrained( + self.model_name, + subfolder="transformer", + quantization_config=BitsAndBytesConfig(load_in_8bit=True), + device_map=torch_device, + ) + pipeline_8bit = DiffusionPipeline.from_pretrained( + self.model_name, transformer=model_8bit, torch_dtype=torch.float16 + ) + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + logger.setLevel(30) + + with CaptureLogger(logger) as cap_logger: + pipeline_8bit.enable_model_cpu_offload() + + assert "has been loaded in `bitsandbytes` 8bit" in cap_logger.out + + def test_moving_to_cpu_throws_warning(self): + model_8bit = SD3Transformer2DModel.from_pretrained( + self.model_name, + subfolder="transformer", + quantization_config=BitsAndBytesConfig(load_in_8bit=True), + device_map=torch_device, + ) + logger = logging.get_logger("diffusers.pipelines.pipeline_utils") + logger.setLevel(30) + + with CaptureLogger(logger) as cap_logger: + # Because `model.dtype` will return torch.float16 as SD3 transformer has + # a conv layer as the first layer. + _ = DiffusionPipeline.from_pretrained( + self.model_name, transformer=model_8bit, torch_dtype=torch.float16 + ).to("cpu") + + assert "Pipelines loaded with `dtype=torch.float16`" in cap_logger.out + + def test_generate_quality_dequantize(self): + r""" + Test that loading the model and unquantize it produce correct results. + """ + self.pipeline_8bit.transformer.dequantize() + output = self.pipeline_8bit( + prompt=self.prompt, + num_inference_steps=self.num_inference_steps, + generator=torch.manual_seed(self.seed), + output_type="np", + ).images + + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.0266, 0.0264, 0.0271, 0.0110, 0.0310, 0.0098, 0.0078, 0.0256, 0.0208]) + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-2) + + # 8bit models cannot be offloaded to CPU. + self.assertTrue(self.pipeline_8bit.transformer.device.type == torch_device) + # calling it again shouldn't be a problem + _ = self.pipeline_8bit( + prompt=self.prompt, + num_inference_steps=2, + generator=torch.manual_seed(self.seed), + output_type="np", + ).images + + @pytest.mark.xfail( + condition=is_accelerate_version("<=", "1.1.1"), + reason="Test will pass after https://github.com/huggingface/accelerate/pull/3223 is in a release.", + strict=True, + ) + def test_pipeline_cuda_placement_works_with_mixed_int8(self): + transformer_8bit_config = BitsAndBytesConfig(load_in_8bit=True) + transformer_8bit = SD3Transformer2DModel.from_pretrained( + self.model_name, + subfolder="transformer", + quantization_config=transformer_8bit_config, + torch_dtype=torch.float16, + device_map=torch_device, + ) + text_encoder_3_8bit_config = BnbConfig(load_in_8bit=True) + text_encoder_3_8bit = T5EncoderModel.from_pretrained( + self.model_name, + subfolder="text_encoder_3", + quantization_config=text_encoder_3_8bit_config, + torch_dtype=torch.float16, + device_map=torch_device, + ) + + # CUDA device placement works. + device = torch_device if torch_device != "rocm" else "cuda" + pipeline_8bit = DiffusionPipeline.from_pretrained( + self.model_name, + transformer=transformer_8bit, + text_encoder_3=text_encoder_3_8bit, + torch_dtype=torch.float16, + ).to(device) + + # Check if inference works. + _ = pipeline_8bit(self.prompt, max_sequence_length=20, num_inference_steps=2) + + del pipeline_8bit + + def test_device_map(self): + """ + Test if the quantized model is working properly with "auto" + pu/disk offloading doesn't work with bnb. + """ + + def get_dummy_tensor_inputs(device=None, seed: int = 0): + batch_size = 1 + num_latent_channels = 4 + num_image_channels = 3 + height = width = 4 + sequence_length = 48 + embedding_dim = 32 + + torch.manual_seed(seed) + hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to( + device, dtype=torch.bfloat16 + ) + + torch.manual_seed(seed) + encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to( + device, dtype=torch.bfloat16 + ) + + torch.manual_seed(seed) + pooled_prompt_embeds = torch.randn((batch_size, embedding_dim)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + text_ids = torch.randn((sequence_length, num_image_channels)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + image_ids = torch.randn((height * width, num_image_channels)).to(device, dtype=torch.bfloat16) + + timestep = torch.tensor([1.0]).to(device, dtype=torch.bfloat16).expand(batch_size) + + return { + "hidden_states": hidden_states, + "encoder_hidden_states": encoder_hidden_states, + "pooled_projections": pooled_prompt_embeds, + "txt_ids": text_ids, + "img_ids": image_ids, + "timestep": timestep, + } + + inputs = get_dummy_tensor_inputs(torch_device) + expected_slice = np.array( + [ + 0.33789062, + -0.04736328, + -0.00256348, + -0.23144531, + -0.49804688, + 0.4375, + -0.15429688, + -0.65234375, + 0.44335938, + ] + ) + + # non sharded + quantization_config = BitsAndBytesConfig(load_in_8bit=True) + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + subfolder="transformer", + quantization_config=quantization_config, + device_map="auto", + torch_dtype=torch.bfloat16, + ) + + weight = quantized_model.transformer_blocks[0].ff.net[2].weight + self.assertTrue(isinstance(weight, bnb.nn.modules.Int8Params)) + + output = quantized_model(**inputs)[0] + output_slice = output.flatten()[-9:].detach().float().cpu().numpy() + self.assertTrue(numpy_cosine_similarity_distance(output_slice, expected_slice) < 1e-3) + + # sharded + quantization_config = BitsAndBytesConfig(load_in_8bit=True) + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-sharded", + subfolder="transformer", + quantization_config=quantization_config, + device_map="auto", + torch_dtype=torch.bfloat16, + ) + + weight = quantized_model.transformer_blocks[0].ff.net[2].weight + self.assertTrue(isinstance(weight, bnb.nn.modules.Int8Params)) + output = quantized_model(**inputs)[0] + output_slice = output.flatten()[-9:].detach().float().cpu().numpy() + + self.assertTrue(numpy_cosine_similarity_distance(output_slice, expected_slice) < 1e-3) + + +@require_transformers_version_greater("4.44.0") +class SlowBnb8bitFluxTests(Base8bitTests): + def setUp(self) -> None: + gc.collect() + backend_empty_cache(torch_device) + + model_id = "hf-internal-testing/flux.1-dev-int8-pkg" + t5_8bit = T5EncoderModel.from_pretrained(model_id, subfolder="text_encoder_2") + transformer_8bit = FluxTransformer2DModel.from_pretrained(model_id, subfolder="transformer") + self.pipeline_8bit = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + text_encoder_2=t5_8bit, + transformer=transformer_8bit, + torch_dtype=torch.float16, + ) + self.pipeline_8bit.enable_model_cpu_offload() + + def tearDown(self): + del self.pipeline_8bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_quality(self): + # keep the resolution and max tokens to a lower number for faster execution. + output = self.pipeline_8bit( + prompt=self.prompt, + num_inference_steps=self.num_inference_steps, + generator=torch.manual_seed(self.seed), + height=256, + width=256, + max_sequence_length=64, + output_type="np", + ).images + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.0574, 0.0554, 0.0581, 0.0686, 0.0676, 0.0759, 0.0757, 0.0803, 0.0930]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-3) + + @require_peft_version_greater("0.14.0") + def test_lora_loading(self): + self.pipeline_8bit.load_lora_weights( + hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd" + ) + self.pipeline_8bit.set_adapters("hyper-sd", adapter_weights=0.125) + + output = self.pipeline_8bit( + prompt=self.prompt, + height=256, + width=256, + max_sequence_length=64, + output_type="np", + num_inference_steps=8, + generator=torch.manual_seed(42), + ).images + out_slice = output[0, -3:, -3:, -1].flatten() + + expected_slice = np.array([0.3916, 0.3916, 0.3887, 0.4243, 0.4155, 0.4233, 0.4570, 0.4531, 0.4248]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-3) + + +@require_transformers_version_greater("4.44.0") +@require_peft_backend +class SlowBnb4BitFluxControlWithLoraTests(Base8bitTests): + def setUp(self) -> None: + gc.collect() + backend_empty_cache(torch_device) + + self.pipeline_8bit = FluxControlPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + quantization_config=PipelineQuantizationConfig( + quant_backend="bitsandbytes_8bit", + quant_kwargs={"load_in_8bit": True}, + components_to_quantize=["transformer", "text_encoder_2"], + ), + torch_dtype=torch.float16, + ) + self.pipeline_8bit.enable_model_cpu_offload() + + def tearDown(self): + del self.pipeline_8bit + + gc.collect() + backend_empty_cache(torch_device) + + def test_lora_loading(self): + self.pipeline_8bit.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora") + + output = self.pipeline_8bit( + prompt=self.prompt, + control_image=Image.new(mode="RGB", size=(256, 256)), + height=256, + width=256, + max_sequence_length=64, + output_type="np", + num_inference_steps=8, + generator=torch.Generator().manual_seed(42), + ).images + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.2029, 0.2136, 0.2268, 0.1921, 0.1997, 0.2185, 0.2021, 0.2183, 0.2292]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-3, msg=f"{out_slice=} != {expected_slice=}") + + +@slow +class BaseBnb8bitSerializationTests(Base8bitTests): + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + quantization_config = BitsAndBytesConfig( + load_in_8bit=True, + ) + self.model_0 = SD3Transformer2DModel.from_pretrained( + self.model_name, subfolder="transformer", quantization_config=quantization_config, device_map=torch_device + ) + + def tearDown(self): + del self.model_0 + + gc.collect() + backend_empty_cache(torch_device) + + def test_serialization(self): + r""" + Test whether it is possible to serialize a model in 8-bit. Uses most typical params as default. + """ + self.assertTrue("_pre_quantization_dtype" in self.model_0.config) + with tempfile.TemporaryDirectory() as tmpdirname: + self.model_0.save_pretrained(tmpdirname) + + config = SD3Transformer2DModel.load_config(tmpdirname) + self.assertTrue("quantization_config" in config) + self.assertTrue("_pre_quantization_dtype" not in config) + + model_1 = SD3Transformer2DModel.from_pretrained(tmpdirname) + + # checking quantized linear module weight + linear = get_some_linear_layer(model_1) + self.assertTrue(linear.weight.__class__ == bnb.nn.Int8Params) + self.assertTrue(hasattr(linear.weight, "SCB")) + + # checking memory footpring + self.assertAlmostEqual(self.model_0.get_memory_footprint() / model_1.get_memory_footprint(), 1, places=2) + + # Matching all parameters and their quant_state items: + d0 = dict(self.model_0.named_parameters()) + d1 = dict(model_1.named_parameters()) + self.assertTrue(d0.keys() == d1.keys()) + + # comparing forward() outputs + dummy_inputs = self.get_dummy_inputs() + inputs = {k: v.to(torch_device) for k, v in dummy_inputs.items() if isinstance(v, torch.Tensor)} + inputs.update({k: v for k, v in dummy_inputs.items() if k not in inputs}) + out_0 = self.model_0(**inputs)[0] + out_1 = model_1(**inputs)[0] + self.assertTrue(torch.equal(out_0, out_1)) + + def test_serialization_sharded(self): + with tempfile.TemporaryDirectory() as tmpdirname: + self.model_0.save_pretrained(tmpdirname, max_shard_size="200MB") + + config = SD3Transformer2DModel.load_config(tmpdirname) + self.assertTrue("quantization_config" in config) + self.assertTrue("_pre_quantization_dtype" not in config) + + model_1 = SD3Transformer2DModel.from_pretrained(tmpdirname) + + # checking quantized linear module weight + linear = get_some_linear_layer(model_1) + self.assertTrue(linear.weight.__class__ == bnb.nn.Int8Params) + self.assertTrue(hasattr(linear.weight, "SCB")) + + # comparing forward() outputs + dummy_inputs = self.get_dummy_inputs() + inputs = {k: v.to(torch_device) for k, v in dummy_inputs.items() if isinstance(v, torch.Tensor)} + inputs.update({k: v for k, v in dummy_inputs.items() if k not in inputs}) + out_0 = self.model_0(**inputs)[0] + out_1 = model_1(**inputs)[0] + self.assertTrue(torch.equal(out_0, out_1)) + + +@require_torch_version_greater_equal("2.6.0") +@require_bitsandbytes_version_greater("0.48.0") +class Bnb8BitCompileTests(QuantCompileTests, unittest.TestCase): + @property + def quantization_config(self): + return PipelineQuantizationConfig( + quant_backend="bitsandbytes_8bit", + quant_kwargs={"load_in_8bit": True}, + components_to_quantize=["transformer", "text_encoder_2"], + ) + + @pytest.mark.xfail( + reason="Test fails because of a type change when recompiling." + " Test passes without recompilation context manager. Refer to https://github.com/huggingface/diffusers/pull/12002/files#r2240462757 for details." + ) + def test_torch_compile(self): + torch._dynamo.config.capture_dynamic_output_shape_ops = True + super()._test_torch_compile(torch_dtype=torch.float16) + + def test_torch_compile_with_cpu_offload(self): + super()._test_torch_compile_with_cpu_offload(torch_dtype=torch.float16) + + def test_torch_compile_with_group_offload_leaf(self): + super()._test_torch_compile_with_group_offload_leaf(torch_dtype=torch.float16, use_stream=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/gguf/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/gguf/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/gguf/test_gguf.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/gguf/test_gguf.py new file mode 100644 index 0000000000000000000000000000000000000000..b42764be10d6b1e85bd112647467bd638a0dac1d --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/gguf/test_gguf.py @@ -0,0 +1,768 @@ +import gc +import unittest + +import numpy as np +import torch +import torch.nn as nn + +from diffusers import ( + AuraFlowPipeline, + AuraFlowTransformer2DModel, + DiffusionPipeline, + FluxControlPipeline, + FluxPipeline, + FluxTransformer2DModel, + GGUFQuantizationConfig, + HiDreamImageTransformer2DModel, + SD3Transformer2DModel, + StableDiffusion3Pipeline, + WanAnimateTransformer3DModel, + WanTransformer3DModel, + WanVACETransformer3DModel, +) +from diffusers.utils import load_image + +from ...testing_utils import ( + Expectations, + backend_empty_cache, + backend_max_memory_allocated, + backend_reset_peak_memory_stats, + enable_full_determinism, + is_gguf_available, + nightly, + numpy_cosine_similarity_distance, + require_accelerate, + require_accelerator, + require_big_accelerator, + require_gguf_version_greater_or_equal, + require_kernels_version_greater_or_equal, + require_peft_backend, + require_torch_version_greater, + torch_device, +) +from ..test_torch_compile_utils import QuantCompileTests + + +if is_gguf_available(): + import gguf + + from diffusers.quantizers.gguf.utils import GGUFLinear, GGUFParameter + +enable_full_determinism() + + +@nightly +@require_accelerate +@require_accelerator +@require_gguf_version_greater_or_equal("0.10.0") +@require_kernels_version_greater_or_equal("0.9.0") +class GGUFCudaKernelsTests(unittest.TestCase): + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def test_cuda_kernels_vs_native(self): + if torch_device != "cuda": + self.skipTest("CUDA kernels test requires CUDA device") + + from diffusers.quantizers.gguf.utils import GGUFLinear, can_use_cuda_kernels + + if not can_use_cuda_kernels: + self.skipTest("CUDA kernels not available (compute capability < 7 or kernels not installed)") + + test_quant_types = ["Q4_0", "Q4_K"] + test_shape = (1, 64, 512) # batch, seq_len, hidden_dim + compute_dtype = torch.bfloat16 + + for quant_type in test_quant_types: + qtype = getattr(gguf.GGMLQuantizationType, quant_type) + in_features, out_features = 512, 512 + + torch.manual_seed(42) + float_weight = torch.randn(out_features, in_features, dtype=torch.float32) + quantized_data = gguf.quants.quantize(float_weight.numpy(), qtype) + weight_data = torch.from_numpy(quantized_data).to(device=torch_device) + weight = GGUFParameter(weight_data, quant_type=qtype) + + x = torch.randn(test_shape, dtype=compute_dtype, device=torch_device) + + linear = GGUFLinear(in_features, out_features, bias=True, compute_dtype=compute_dtype) + linear.weight = weight + linear.bias = nn.Parameter(torch.randn(out_features, dtype=compute_dtype)) + linear = linear.to(torch_device) + + with torch.no_grad(): + output_native = linear.forward_native(x) + output_cuda = linear.forward_cuda(x) + + assert torch.allclose(output_native, output_cuda, 1e-2), ( + f"GGUF CUDA Kernel Output is different from Native Output for {quant_type}" + ) + + +@nightly +@require_big_accelerator +@require_accelerate +@require_gguf_version_greater_or_equal("0.10.0") +class GGUFSingleFileTesterMixin: + ckpt_path = None + model_cls = None + torch_dtype = torch.bfloat16 + expected_memory_use_in_gb = 5 + + def test_gguf_parameters(self): + quant_storage_type = torch.uint8 + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config) + + for param_name, param in model.named_parameters(): + if isinstance(param, GGUFParameter): + assert hasattr(param, "quant_type") + assert param.dtype == quant_storage_type + + def test_gguf_linear_layers(self): + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config) + + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Linear) and hasattr(module.weight, "quant_type"): + assert module.weight.dtype == torch.uint8 + if module.bias is not None: + assert module.bias.dtype == self.torch_dtype + + def test_gguf_memory_usage(self): + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + + model = self.model_cls.from_single_file( + self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype + ) + model.to(torch_device) + assert (model.get_memory_footprint() / 1024**3) < self.expected_memory_use_in_gb + inputs = self.get_dummy_inputs() + + backend_reset_peak_memory_stats(torch_device) + backend_empty_cache(torch_device) + with torch.no_grad(): + model(**inputs) + max_memory = backend_max_memory_allocated(torch_device) + assert (max_memory / 1024**3) < self.expected_memory_use_in_gb + + def test_keep_modules_in_fp32(self): + r""" + A simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32. + Also ensures if inference works. + """ + _keep_in_fp32_modules = self.model_cls._keep_in_fp32_modules + self.model_cls._keep_in_fp32_modules = ["proj_out"] + + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config) + + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Linear): + if name in model._keep_in_fp32_modules: + assert module.weight.dtype == torch.float32 + self.model_cls._keep_in_fp32_modules = _keep_in_fp32_modules + + def test_dtype_assignment(self): + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config) + + with self.assertRaises(ValueError): + # Tries with a `dtype` + model.to(torch.float16) + + with self.assertRaises(ValueError): + # Tries with a `device` and `dtype` + device_0 = f"{torch_device}:0" + model.to(device=device_0, dtype=torch.float16) + + with self.assertRaises(ValueError): + # Tries with a cast + model.float() + + with self.assertRaises(ValueError): + # Tries with a cast + model.half() + + # This should work + model.to(torch_device) + + def test_dequantize_model(self): + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config) + model.dequantize() + + def _check_for_gguf_linear(model): + has_children = list(model.children()) + if not has_children: + return + + for name, module in model.named_children(): + if isinstance(module, nn.Linear): + assert not isinstance(module, GGUFLinear), f"{name} is still GGUFLinear" + assert not isinstance(module.weight, GGUFParameter), f"{name} weight is still GGUFParameter" + + for name, module in model.named_children(): + _check_for_gguf_linear(module) + + +class FluxGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf" + diffusers_ckpt_path = "https://huggingface.co/sayakpaul/flux-diffusers-gguf/blob/main/model-Q4_0.gguf" + torch_dtype = torch.bfloat16 + model_cls = FluxTransformer2DModel + expected_memory_use_in_gb = 5 + + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 4096, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "pooled_projections": torch.randn( + (1, 768), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + "img_ids": torch.randn((4096, 3), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "txt_ids": torch.randn((512, 3), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "guidance": torch.tensor([3.5]).to(torch_device, self.torch_dtype), + } + + def test_pipeline_inference(self): + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + transformer = self.model_cls.from_single_file( + self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype + ) + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=self.torch_dtype + ) + pipe.enable_model_cpu_offload() + + prompt = "a cat holding a sign that says hello" + output = pipe( + prompt=prompt, num_inference_steps=2, generator=torch.Generator("cpu").manual_seed(0), output_type="np" + ).images[0] + output_slice = output[:3, :3, :].flatten() + expected_slice = np.array( + [ + 0.47265625, + 0.43359375, + 0.359375, + 0.47070312, + 0.421875, + 0.34375, + 0.46875, + 0.421875, + 0.34765625, + 0.46484375, + 0.421875, + 0.34179688, + 0.47070312, + 0.42578125, + 0.34570312, + 0.46875, + 0.42578125, + 0.3515625, + 0.45507812, + 0.4140625, + 0.33984375, + 0.4609375, + 0.41796875, + 0.34375, + 0.45898438, + 0.41796875, + 0.34375, + ] + ) + max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice) + assert max_diff < 1e-4 + + def test_loading_gguf_diffusers_format(self): + model = self.model_cls.from_single_file( + self.diffusers_ckpt_path, + subfolder="transformer", + quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16), + config="black-forest-labs/FLUX.1-dev", + ) + model.to(torch_device) + model(**self.get_dummy_inputs()) + + +class SD35LargeGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/city96/stable-diffusion-3.5-large-gguf/blob/main/sd3.5_large-Q4_0.gguf" + torch_dtype = torch.bfloat16 + model_cls = SD3Transformer2DModel + expected_memory_use_in_gb = 5 + + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 16, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "pooled_projections": torch.randn( + (1, 2048), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + } + + def test_pipeline_inference(self): + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + transformer = self.model_cls.from_single_file( + self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype + ) + pipe = StableDiffusion3Pipeline.from_pretrained( + "stabilityai/stable-diffusion-3.5-large", transformer=transformer, torch_dtype=self.torch_dtype + ) + pipe.enable_model_cpu_offload() + + prompt = "a cat holding a sign that says hello" + output = pipe( + prompt=prompt, + num_inference_steps=2, + generator=torch.Generator("cpu").manual_seed(0), + output_type="np", + ).images[0] + output_slice = output[:3, :3, :].flatten() + expected_slices = Expectations( + { + ("xpu", 3): np.array( + [ + 0.16796875, + 0.27929688, + 0.28320312, + 0.11328125, + 0.27539062, + 0.26171875, + 0.10742188, + 0.26367188, + 0.26171875, + 0.1484375, + 0.2734375, + 0.296875, + 0.13476562, + 0.2890625, + 0.30078125, + 0.1171875, + 0.28125, + 0.28125, + 0.16015625, + 0.31445312, + 0.30078125, + 0.15625, + 0.32421875, + 0.296875, + 0.14453125, + 0.30859375, + 0.2890625, + ] + ), + ("cuda", 7): np.array( + [ + 0.17578125, + 0.27539062, + 0.27734375, + 0.11914062, + 0.26953125, + 0.25390625, + 0.109375, + 0.25390625, + 0.25, + 0.15039062, + 0.26171875, + 0.28515625, + 0.13671875, + 0.27734375, + 0.28515625, + 0.12109375, + 0.26757812, + 0.265625, + 0.16210938, + 0.29882812, + 0.28515625, + 0.15625, + 0.30664062, + 0.27734375, + 0.14648438, + 0.29296875, + 0.26953125, + ] + ), + } + ) + expected_slice = expected_slices.get_expectation() + max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice) + assert max_diff < 1e-4 + + +class SD35MediumGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/city96/stable-diffusion-3.5-medium-gguf/blob/main/sd3.5_medium-Q3_K_M.gguf" + torch_dtype = torch.bfloat16 + model_cls = SD3Transformer2DModel + expected_memory_use_in_gb = 2 + + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 16, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "pooled_projections": torch.randn( + (1, 2048), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + } + + def test_pipeline_inference(self): + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + transformer = self.model_cls.from_single_file( + self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype + ) + pipe = StableDiffusion3Pipeline.from_pretrained( + "stabilityai/stable-diffusion-3.5-medium", transformer=transformer, torch_dtype=self.torch_dtype + ) + pipe.enable_model_cpu_offload() + + prompt = "a cat holding a sign that says hello" + output = pipe( + prompt=prompt, num_inference_steps=2, generator=torch.Generator("cpu").manual_seed(0), output_type="np" + ).images[0] + output_slice = output[:3, :3, :].flatten() + expected_slice = np.array( + [ + 0.625, + 0.6171875, + 0.609375, + 0.65625, + 0.65234375, + 0.640625, + 0.6484375, + 0.640625, + 0.625, + 0.6484375, + 0.63671875, + 0.6484375, + 0.66796875, + 0.65625, + 0.65234375, + 0.6640625, + 0.6484375, + 0.6328125, + 0.6640625, + 0.6484375, + 0.640625, + 0.67578125, + 0.66015625, + 0.62109375, + 0.671875, + 0.65625, + 0.62109375, + ] + ) + max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice) + assert max_diff < 1e-4 + + +class AuraFlowGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/city96/AuraFlow-v0.3-gguf/blob/main/aura_flow_0.3-Q2_K.gguf" + torch_dtype = torch.bfloat16 + model_cls = AuraFlowTransformer2DModel + expected_memory_use_in_gb = 4 + + def setUp(self): + gc.collect() + backend_empty_cache(torch_device) + + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 4, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 2048), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + } + + def test_pipeline_inference(self): + quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + transformer = self.model_cls.from_single_file( + self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype + ) + pipe = AuraFlowPipeline.from_pretrained( + "fal/AuraFlow-v0.3", transformer=transformer, torch_dtype=self.torch_dtype + ) + pipe.enable_model_cpu_offload() + + prompt = "a pony holding a sign that says hello" + output = pipe( + prompt=prompt, num_inference_steps=2, generator=torch.Generator("cpu").manual_seed(0), output_type="np" + ).images[0] + output_slice = output[:3, :3, :].flatten() + expected_slice = np.array( + [ + 0.46484375, + 0.546875, + 0.64453125, + 0.48242188, + 0.53515625, + 0.59765625, + 0.47070312, + 0.5078125, + 0.5703125, + 0.42773438, + 0.50390625, + 0.5703125, + 0.47070312, + 0.515625, + 0.57421875, + 0.45898438, + 0.48632812, + 0.53515625, + 0.4453125, + 0.5078125, + 0.56640625, + 0.47851562, + 0.5234375, + 0.57421875, + 0.48632812, + 0.5234375, + 0.56640625, + ] + ) + max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice) + assert max_diff < 1e-4 + + +@require_peft_backend +@nightly +@require_big_accelerator +@require_accelerate +@require_gguf_version_greater_or_equal("0.10.0") +class FluxControlLoRAGGUFTests(unittest.TestCase): + def test_lora_loading(self): + ckpt_path = "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf" + transformer = FluxTransformer2DModel.from_single_file( + ckpt_path, + quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16), + torch_dtype=torch.bfloat16, + ) + pipe = FluxControlPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + transformer=transformer, + torch_dtype=torch.bfloat16, + ).to(torch_device) + pipe.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora") + + prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts." + control_image = load_image( + "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/control_image_robot_canny.png" + ) + + output = pipe( + prompt=prompt, + control_image=control_image, + height=256, + width=256, + num_inference_steps=10, + guidance_scale=30.0, + output_type="np", + generator=torch.manual_seed(0), + ).images + + out_slice = output[0, -3:, -3:, -1].flatten() + expected_slice = np.array([0.8047, 0.8359, 0.8711, 0.6875, 0.7070, 0.7383, 0.5469, 0.5820, 0.6641]) + + max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice) + self.assertTrue(max_diff < 1e-3) + + +class HiDreamGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/city96/HiDream-I1-Dev-gguf/blob/main/hidream-i1-dev-Q2_K.gguf" + torch_dtype = torch.bfloat16 + model_cls = HiDreamImageTransformer2DModel + expected_memory_use_in_gb = 8 + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 16, 128, 128), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states_t5": torch.randn( + (1, 128, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "encoder_hidden_states_llama3": torch.randn( + (32, 1, 128, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "pooled_embeds": torch.randn( + (1, 2048), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timesteps": torch.tensor([1]).to(torch_device, self.torch_dtype), + } + + +class WanGGUFTexttoVideoSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/city96/Wan2.1-T2V-14B-gguf/blob/main/wan2.1-t2v-14b-Q3_K_S.gguf" + torch_dtype = torch.bfloat16 + model_cls = WanTransformer3DModel + expected_memory_use_in_gb = 9 + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 16, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + } + + +class WanGGUFImagetoVideoSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/city96/Wan2.1-I2V-14B-480P-gguf/blob/main/wan2.1-i2v-14b-480p-Q3_K_S.gguf" + torch_dtype = torch.bfloat16 + model_cls = WanTransformer3DModel + expected_memory_use_in_gb = 9 + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 36, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "encoder_hidden_states_image": torch.randn( + (1, 257, 1280), generator=torch.Generator("cpu").manual_seed(0) + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + } + + +class WanVACEGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/QuantStack/Wan2.1_14B_VACE-GGUF/blob/main/Wan2.1_14B_VACE-Q3_K_S.gguf" + torch_dtype = torch.bfloat16 + model_cls = WanVACETransformer3DModel + expected_memory_use_in_gb = 9 + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 16, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "control_hidden_states": torch.randn( + (1, 96, 2, 64, 64), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "control_hidden_states_scale": torch.randn( + (8,), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + } + + +class WanAnimateGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase): + ckpt_path = "https://huggingface.co/QuantStack/Wan2.2-Animate-14B-GGUF/blob/main/Wan2.2-Animate-14B-Q3_K_S.gguf" + torch_dtype = torch.bfloat16 + model_cls = WanAnimateTransformer3DModel + expected_memory_use_in_gb = 9 + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 16, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "control_hidden_states": torch.randn( + (1, 96, 2, 64, 64), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "control_hidden_states_scale": torch.randn( + (8,), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + } + + +@require_torch_version_greater("2.7.1") +class GGUFCompileTests(QuantCompileTests, unittest.TestCase): + torch_dtype = torch.bfloat16 + gguf_ckpt = "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf" + + @property + def quantization_config(self): + return GGUFQuantizationConfig(compute_dtype=self.torch_dtype) + + def _init_pipeline(self, *args, **kwargs): + transformer = FluxTransformer2DModel.from_single_file( + self.gguf_ckpt, quantization_config=self.quantization_config, torch_dtype=self.torch_dtype + ) + pipe = DiffusionPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=self.torch_dtype + ) + return pipe diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/modelopt/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/modelopt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/modelopt/test_modelopt.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/modelopt/test_modelopt.py new file mode 100644 index 0000000000000000000000000000000000000000..6b0624a280834d4035e216847925428e64b5d8a4 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/modelopt/test_modelopt.py @@ -0,0 +1,306 @@ +import gc +import tempfile +import unittest + +from diffusers import NVIDIAModelOptConfig, SD3Transformer2DModel, StableDiffusion3Pipeline +from diffusers.utils import is_nvidia_modelopt_available, is_torch_available +from diffusers.utils.testing_utils import ( + backend_empty_cache, + backend_reset_peak_memory_stats, + enable_full_determinism, + nightly, + numpy_cosine_similarity_distance, + require_accelerate, + require_big_accelerator, + require_modelopt_version_greater_or_equal, + require_torch_cuda_compatibility, + torch_device, +) + + +if is_nvidia_modelopt_available(): + import modelopt.torch.quantization as mtq + +if is_torch_available(): + import torch + + from ..utils import LoRALayer, get_memory_consumption_stat + +enable_full_determinism() + + +@nightly +@require_big_accelerator +@require_accelerate +@require_modelopt_version_greater_or_equal("0.33.1") +class ModelOptBaseTesterMixin: + model_id = "hf-internal-testing/tiny-sd3-pipe" + model_cls = SD3Transformer2DModel + pipeline_cls = StableDiffusion3Pipeline + torch_dtype = torch.bfloat16 + expected_memory_reduction = 0.0 + keep_in_fp32_module = "" + modules_to_not_convert = "" + _test_torch_compile = False + + def setUp(self): + backend_reset_peak_memory_stats(torch_device) + backend_empty_cache(torch_device) + gc.collect() + + def tearDown(self): + backend_reset_peak_memory_stats(torch_device) + backend_empty_cache(torch_device) + gc.collect() + + def get_dummy_init_kwargs(self): + return {"quant_type": "FP8"} + + def get_dummy_model_init_kwargs(self): + return { + "pretrained_model_name_or_path": self.model_id, + "torch_dtype": self.torch_dtype, + "quantization_config": NVIDIAModelOptConfig(**self.get_dummy_init_kwargs()), + "subfolder": "transformer", + } + + def test_modelopt_layers(self): + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Linear): + assert mtq.utils.is_quantized(module) + + def test_modelopt_memory_usage(self): + inputs = self.get_dummy_inputs() + inputs = { + k: v.to(device=torch_device, dtype=torch.bfloat16) for k, v in inputs.items() if not isinstance(v, bool) + } + + unquantized_model = self.model_cls.from_pretrained( + self.model_id, torch_dtype=self.torch_dtype, subfolder="transformer" + ) + unquantized_model.to(torch_device) + unquantized_model_memory = get_memory_consumption_stat(unquantized_model, inputs) + + quantized_model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + quantized_model.to(torch_device) + quantized_model_memory = get_memory_consumption_stat(quantized_model, inputs) + + assert unquantized_model_memory / quantized_model_memory >= self.expected_memory_reduction + + def test_keep_modules_in_fp32(self): + _keep_in_fp32_modules = self.model_cls._keep_in_fp32_modules + self.model_cls._keep_in_fp32_modules = self.keep_in_fp32_module + + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + model.to(torch_device) + + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Linear): + if name in model._keep_in_fp32_modules: + assert module.weight.dtype == torch.float32 + self.model_cls._keep_in_fp32_modules = _keep_in_fp32_modules + + def test_modules_to_not_convert(self): + init_kwargs = self.get_dummy_model_init_kwargs() + quantization_config_kwargs = self.get_dummy_init_kwargs() + quantization_config_kwargs.update({"modules_to_not_convert": self.modules_to_not_convert}) + quantization_config = NVIDIAModelOptConfig(**quantization_config_kwargs) + init_kwargs.update({"quantization_config": quantization_config}) + + model = self.model_cls.from_pretrained(**init_kwargs) + model.to(torch_device) + + for name, module in model.named_modules(): + if name in self.modules_to_not_convert: + assert not mtq.utils.is_quantized(module) + + def test_dtype_assignment(self): + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + + with self.assertRaises(ValueError): + model.to(torch.float16) + + with self.assertRaises(ValueError): + device_0 = f"{torch_device}:0" + model.to(device=device_0, dtype=torch.float16) + + with self.assertRaises(ValueError): + model.float() + + with self.assertRaises(ValueError): + model.half() + + model.to(torch_device) + + def test_serialization(self): + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + inputs = self.get_dummy_inputs() + + model.to(torch_device) + with torch.no_grad(): + model_output = model(**inputs) + + with tempfile.TemporaryDirectory() as tmp_dir: + model.save_pretrained(tmp_dir) + saved_model = self.model_cls.from_pretrained( + tmp_dir, + torch_dtype=torch.bfloat16, + ) + + saved_model.to(torch_device) + with torch.no_grad(): + saved_model_output = saved_model(**inputs) + + assert torch.allclose(model_output.sample, saved_model_output.sample, rtol=1e-5, atol=1e-5) + + def test_torch_compile(self): + if not self._test_torch_compile: + return + + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + compiled_model = torch.compile(model, mode="max-autotune", fullgraph=True, dynamic=False) + + model.to(torch_device) + with torch.no_grad(): + model_output = model(**self.get_dummy_inputs()).sample + + compiled_model.to(torch_device) + with torch.no_grad(): + compiled_model_output = compiled_model(**self.get_dummy_inputs()).sample + + model_output = model_output.detach().float().cpu().numpy() + compiled_model_output = compiled_model_output.detach().float().cpu().numpy() + + max_diff = numpy_cosine_similarity_distance(model_output.flatten(), compiled_model_output.flatten()) + assert max_diff < 1e-3 + + def test_device_map_error(self): + with self.assertRaises(ValueError): + _ = self.model_cls.from_pretrained( + **self.get_dummy_model_init_kwargs(), + device_map={0: "8GB", "cpu": "16GB"}, + ) + + def get_dummy_inputs(self): + batch_size = 1 + seq_len = 16 + height = width = 32 + num_latent_channels = 4 + caption_channels = 8 + + torch.manual_seed(0) + hidden_states = torch.randn((batch_size, num_latent_channels, height, width)).to( + torch_device, dtype=torch.bfloat16 + ) + encoder_hidden_states = torch.randn((batch_size, seq_len, caption_channels)).to( + torch_device, dtype=torch.bfloat16 + ) + timestep = torch.tensor([1.0]).to(torch_device, dtype=torch.bfloat16).expand(batch_size) + + return { + "hidden_states": hidden_states, + "encoder_hidden_states": encoder_hidden_states, + "timestep": timestep, + } + + def test_model_cpu_offload(self): + init_kwargs = self.get_dummy_init_kwargs() + transformer = self.model_cls.from_pretrained( + self.model_id, + quantization_config=NVIDIAModelOptConfig(**init_kwargs), + subfolder="transformer", + torch_dtype=torch.bfloat16, + ) + pipe = self.pipeline_cls.from_pretrained(self.model_id, transformer=transformer, torch_dtype=torch.bfloat16) + pipe.enable_model_cpu_offload(device=torch_device) + _ = pipe("a cat holding a sign that says hello", num_inference_steps=2) + + def test_training(self): + quantization_config = NVIDIAModelOptConfig(**self.get_dummy_init_kwargs()) + quantized_model = self.model_cls.from_pretrained( + self.model_id, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + ).to(torch_device) + + for param in quantized_model.parameters(): + param.requires_grad = False + if param.ndim == 1: + param.data = param.data.to(torch.float32) + + for _, module in quantized_model.named_modules(): + if hasattr(module, "to_q"): + module.to_q = LoRALayer(module.to_q, rank=4) + if hasattr(module, "to_k"): + module.to_k = LoRALayer(module.to_k, rank=4) + if hasattr(module, "to_v"): + module.to_v = LoRALayer(module.to_v, rank=4) + + with torch.amp.autocast(str(torch_device), dtype=torch.bfloat16): + inputs = self.get_dummy_inputs() + output = quantized_model(**inputs)[0] + output.norm().backward() + + for module in quantized_model.modules(): + if isinstance(module, LoRALayer): + self.assertTrue(module.adapter[1].weight.grad is not None) + + +class SanaTransformerFP8WeightsTest(ModelOptBaseTesterMixin, unittest.TestCase): + expected_memory_reduction = 0.6 + + def get_dummy_init_kwargs(self): + return {"quant_type": "FP8"} + + +class SanaTransformerINT8WeightsTest(ModelOptBaseTesterMixin, unittest.TestCase): + expected_memory_reduction = 0.6 + _test_torch_compile = True + + def get_dummy_init_kwargs(self): + return {"quant_type": "INT8"} + + +@require_torch_cuda_compatibility(8.0) +class SanaTransformerINT4WeightsTest(ModelOptBaseTesterMixin, unittest.TestCase): + expected_memory_reduction = 0.55 + + def get_dummy_init_kwargs(self): + return { + "quant_type": "INT4", + "block_quantize": 128, + "channel_quantize": -1, + "disable_conv_quantization": True, + } + + +@require_torch_cuda_compatibility(8.0) +class SanaTransformerNF4WeightsTest(ModelOptBaseTesterMixin, unittest.TestCase): + expected_memory_reduction = 0.65 + + def get_dummy_init_kwargs(self): + return { + "quant_type": "NF4", + "block_quantize": 128, + "channel_quantize": -1, + "scale_block_quantize": 8, + "scale_channel_quantize": -1, + "modules_to_not_convert": ["conv"], + } + + +@require_torch_cuda_compatibility(8.0) +class SanaTransformerNVFP4WeightsTest(ModelOptBaseTesterMixin, unittest.TestCase): + expected_memory_reduction = 0.65 + + def get_dummy_init_kwargs(self): + return { + "quant_type": "NVFP4", + "block_quantize": 128, + "channel_quantize": -1, + "scale_block_quantize": 8, + "scale_channel_quantize": -1, + "modules_to_not_convert": ["conv"], + } diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/quanto/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/quanto/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/quanto/test_quanto.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/quanto/test_quanto.py new file mode 100644 index 0000000000000000000000000000000000000000..e3463f136f942ef38b0a53bd27153535397a2f4b --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/quanto/test_quanto.py @@ -0,0 +1,335 @@ +import gc +import tempfile +import unittest + +from diffusers import FluxPipeline, FluxTransformer2DModel, QuantoConfig +from diffusers.models.attention_processor import Attention +from diffusers.utils import is_optimum_quanto_available, is_torch_available + +from ...testing_utils import ( + backend_empty_cache, + backend_reset_peak_memory_stats, + enable_full_determinism, + nightly, + numpy_cosine_similarity_distance, + require_accelerate, + require_accelerator, + require_torch_cuda_compatibility, + torch_device, +) + + +if is_optimum_quanto_available(): + from optimum.quanto import QLinear + +if is_torch_available(): + import torch + + from ..utils import LoRALayer, get_memory_consumption_stat + +enable_full_determinism() + + +@nightly +@require_accelerator +@require_accelerate +class QuantoBaseTesterMixin: + model_id = None + pipeline_model_id = None + model_cls = None + torch_dtype = torch.bfloat16 + # the expected reduction in peak memory used compared to an unquantized model expressed as a percentage + expected_memory_reduction = 0.0 + keep_in_fp32_module = "" + modules_to_not_convert = "" + _test_torch_compile = False + + def setUp(self): + backend_reset_peak_memory_stats(torch_device) + backend_empty_cache(torch_device) + gc.collect() + + def tearDown(self): + backend_reset_peak_memory_stats(torch_device) + backend_empty_cache(torch_device) + gc.collect() + + def get_dummy_init_kwargs(self): + return {"weights_dtype": "float8"} + + def get_dummy_model_init_kwargs(self): + return { + "pretrained_model_name_or_path": self.model_id, + "torch_dtype": self.torch_dtype, + "quantization_config": QuantoConfig(**self.get_dummy_init_kwargs()), + } + + def test_quanto_layers(self): + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Linear): + assert isinstance(module, QLinear) + + def test_quanto_memory_usage(self): + inputs = self.get_dummy_inputs() + inputs = { + k: v.to(device=torch_device, dtype=torch.bfloat16) for k, v in inputs.items() if not isinstance(v, bool) + } + + unquantized_model = self.model_cls.from_pretrained(self.model_id, torch_dtype=self.torch_dtype) + unquantized_model.to(torch_device) + unquantized_model_memory = get_memory_consumption_stat(unquantized_model, inputs) + + quantized_model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + quantized_model.to(torch_device) + quantized_model_memory = get_memory_consumption_stat(quantized_model, inputs) + + assert unquantized_model_memory / quantized_model_memory >= self.expected_memory_reduction + + def test_keep_modules_in_fp32(self): + r""" + A simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32. + Also ensures if inference works. + """ + _keep_in_fp32_modules = self.model_cls._keep_in_fp32_modules + self.model_cls._keep_in_fp32_modules = self.keep_in_fp32_module + + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + model.to(torch_device) + + for name, module in model.named_modules(): + if isinstance(module, torch.nn.Linear): + if name in model._keep_in_fp32_modules: + assert module.weight.dtype == torch.float32 + self.model_cls._keep_in_fp32_modules = _keep_in_fp32_modules + + def test_modules_to_not_convert(self): + init_kwargs = self.get_dummy_model_init_kwargs() + + quantization_config_kwargs = self.get_dummy_init_kwargs() + quantization_config_kwargs.update({"modules_to_not_convert": self.modules_to_not_convert}) + quantization_config = QuantoConfig(**quantization_config_kwargs) + + init_kwargs.update({"quantization_config": quantization_config}) + + model = self.model_cls.from_pretrained(**init_kwargs) + model.to(torch_device) + + for name, module in model.named_modules(): + if name in self.modules_to_not_convert: + assert not isinstance(module, QLinear) + + def test_dtype_assignment(self): + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + + with self.assertRaises(ValueError): + # Tries with a `dtype` + model.to(torch.float16) + + with self.assertRaises(ValueError): + # Tries with a `device` and `dtype` + device_0 = f"{torch_device}:0" + model.to(device=device_0, dtype=torch.float16) + + with self.assertRaises(ValueError): + # Tries with a cast + model.float() + + with self.assertRaises(ValueError): + # Tries with a cast + model.half() + + # This should work + model.to(torch_device) + + def test_serialization(self): + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + inputs = self.get_dummy_inputs() + + model.to(torch_device) + with torch.no_grad(): + model_output = model(**inputs) + + with tempfile.TemporaryDirectory() as tmp_dir: + model.save_pretrained(tmp_dir) + saved_model = self.model_cls.from_pretrained( + tmp_dir, + torch_dtype=torch.bfloat16, + ) + + saved_model.to(torch_device) + with torch.no_grad(): + saved_model_output = saved_model(**inputs) + + assert torch.allclose(model_output.sample, saved_model_output.sample, rtol=1e-5, atol=1e-5) + + def test_torch_compile(self): + if not self._test_torch_compile: + return + + model = self.model_cls.from_pretrained(**self.get_dummy_model_init_kwargs()) + compiled_model = torch.compile(model, mode="max-autotune", fullgraph=True, dynamic=False) + + model.to(torch_device) + with torch.no_grad(): + model_output = model(**self.get_dummy_inputs()).sample + + compiled_model.to(torch_device) + with torch.no_grad(): + compiled_model_output = compiled_model(**self.get_dummy_inputs()).sample + + model_output = model_output.detach().float().cpu().numpy() + compiled_model_output = compiled_model_output.detach().float().cpu().numpy() + + max_diff = numpy_cosine_similarity_distance(model_output.flatten(), compiled_model_output.flatten()) + assert max_diff < 1e-3 + + def test_device_map_error(self): + with self.assertRaises(ValueError): + _ = self.model_cls.from_pretrained( + **self.get_dummy_model_init_kwargs(), device_map={0: "8GB", "cpu": "16GB"} + ) + + +class FluxTransformerQuantoMixin(QuantoBaseTesterMixin): + model_id = "hf-internal-testing/tiny-flux-transformer" + model_cls = FluxTransformer2DModel + pipeline_cls = FluxPipeline + torch_dtype = torch.bfloat16 + keep_in_fp32_module = "proj_out" + modules_to_not_convert = ["proj_out"] + _test_torch_compile = False + + def get_dummy_inputs(self): + return { + "hidden_states": torch.randn((1, 4096, 64), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "encoder_hidden_states": torch.randn( + (1, 512, 4096), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "pooled_projections": torch.randn( + (1, 768), + generator=torch.Generator("cpu").manual_seed(0), + ).to(torch_device, self.torch_dtype), + "timestep": torch.tensor([1]).to(torch_device, self.torch_dtype), + "img_ids": torch.randn((4096, 3), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "txt_ids": torch.randn((512, 3), generator=torch.Generator("cpu").manual_seed(0)).to( + torch_device, self.torch_dtype + ), + "guidance": torch.tensor([3.5]).to(torch_device, self.torch_dtype), + } + + def get_dummy_training_inputs(self, device=None, seed: int = 0): + batch_size = 1 + num_latent_channels = 4 + num_image_channels = 3 + height = width = 4 + sequence_length = 48 + embedding_dim = 32 + + torch.manual_seed(seed) + hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to( + device, dtype=torch.bfloat16 + ) + + torch.manual_seed(seed) + pooled_prompt_embeds = torch.randn((batch_size, embedding_dim)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + text_ids = torch.randn((sequence_length, num_image_channels)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + image_ids = torch.randn((height * width, num_image_channels)).to(device, dtype=torch.bfloat16) + + timestep = torch.tensor([1.0]).to(device, dtype=torch.bfloat16).expand(batch_size) + + return { + "hidden_states": hidden_states, + "encoder_hidden_states": encoder_hidden_states, + "pooled_projections": pooled_prompt_embeds, + "txt_ids": text_ids, + "img_ids": image_ids, + "timestep": timestep, + } + + def test_model_cpu_offload(self): + init_kwargs = self.get_dummy_init_kwargs() + transformer = self.model_cls.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + quantization_config=QuantoConfig(**init_kwargs), + subfolder="transformer", + torch_dtype=torch.bfloat16, + ) + pipe = self.pipeline_cls.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", transformer=transformer, torch_dtype=torch.bfloat16 + ) + pipe.enable_model_cpu_offload(device=torch_device) + _ = pipe("a cat holding a sign that says hello", num_inference_steps=2) + + def test_training(self): + quantization_config = QuantoConfig(**self.get_dummy_init_kwargs()) + quantized_model = self.model_cls.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + ).to(torch_device) + + for param in quantized_model.parameters(): + # freeze the model as only adapter layers will be trained + param.requires_grad = False + if param.ndim == 1: + param.data = param.data.to(torch.float32) + + for _, module in quantized_model.named_modules(): + if isinstance(module, Attention): + module.to_q = LoRALayer(module.to_q, rank=4) + module.to_k = LoRALayer(module.to_k, rank=4) + module.to_v = LoRALayer(module.to_v, rank=4) + + with torch.amp.autocast(str(torch_device), dtype=torch.bfloat16): + inputs = self.get_dummy_training_inputs(torch_device) + output = quantized_model(**inputs)[0] + output.norm().backward() + + for module in quantized_model.modules(): + if isinstance(module, LoRALayer): + self.assertTrue(module.adapter[1].weight.grad is not None) + + +class FluxTransformerFloat8WeightsTest(FluxTransformerQuantoMixin, unittest.TestCase): + expected_memory_reduction = 0.6 + + def get_dummy_init_kwargs(self): + return {"weights_dtype": "float8"} + + +class FluxTransformerInt8WeightsTest(FluxTransformerQuantoMixin, unittest.TestCase): + expected_memory_reduction = 0.6 + _test_torch_compile = True + + def get_dummy_init_kwargs(self): + return {"weights_dtype": "int8"} + + +@require_torch_cuda_compatibility(8.0) +class FluxTransformerInt4WeightsTest(FluxTransformerQuantoMixin, unittest.TestCase): + expected_memory_reduction = 0.55 + + def get_dummy_init_kwargs(self): + return {"weights_dtype": "int4"} + + +@require_torch_cuda_compatibility(8.0) +class FluxTransformerInt2WeightsTest(FluxTransformerQuantoMixin, unittest.TestCase): + expected_memory_reduction = 0.65 + + def get_dummy_init_kwargs(self): + return {"weights_dtype": "int2"} diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/test_pipeline_level_quantization.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/test_pipeline_level_quantization.py new file mode 100644 index 0000000000000000000000000000000000000000..5f1a3de2e579efc9f5415a5f27e1c0f25259d484 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/test_pipeline_level_quantization.py @@ -0,0 +1,317 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a clone of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import json +import tempfile +import unittest + +import torch +from parameterized import parameterized + +from diffusers import BitsAndBytesConfig, DiffusionPipeline, QuantoConfig +from diffusers.quantizers import PipelineQuantizationConfig +from diffusers.utils import logging + +from ..testing_utils import ( + CaptureLogger, + is_transformers_available, + require_accelerate, + require_bitsandbytes_version_greater, + require_quanto, + require_torch, + require_torch_accelerator, + slow, + torch_device, +) + + +if is_transformers_available(): + from transformers import BitsAndBytesConfig as TranBitsAndBytesConfig +else: + TranBitsAndBytesConfig = None + + +@require_bitsandbytes_version_greater("0.43.2") +@require_quanto +@require_accelerate +@require_torch +@require_torch_accelerator +@slow +class PipelineQuantizationTests(unittest.TestCase): + model_name = "hf-internal-testing/tiny-flux-pipe" + prompt = "a beautiful sunset amidst the mountains." + num_inference_steps = 10 + seed = 0 + + def test_quant_config_set_correctly_through_kwargs(self): + components_to_quantize = ["transformer", "text_encoder_2"] + quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_4bit", + quant_kwargs={ + "load_in_4bit": True, + "bnb_4bit_quant_type": "nf4", + "bnb_4bit_compute_dtype": torch.bfloat16, + }, + components_to_quantize=components_to_quantize, + ) + pipe = DiffusionPipeline.from_pretrained( + self.model_name, + quantization_config=quant_config, + torch_dtype=torch.bfloat16, + ).to(torch_device) + for name, component in pipe.components.items(): + if name in components_to_quantize: + self.assertTrue(getattr(component.config, "quantization_config", None) is not None) + quantization_config = component.config.quantization_config + self.assertTrue(quantization_config.load_in_4bit) + self.assertTrue(quantization_config.quant_method == "bitsandbytes") + + _ = pipe(self.prompt, num_inference_steps=self.num_inference_steps) + + def test_quant_config_set_correctly_through_granular(self): + quant_config = PipelineQuantizationConfig( + quant_mapping={ + "transformer": QuantoConfig(weights_dtype="int8"), + "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16), + } + ) + components_to_quantize = list(quant_config.quant_mapping.keys()) + pipe = DiffusionPipeline.from_pretrained( + self.model_name, + quantization_config=quant_config, + torch_dtype=torch.bfloat16, + ).to(torch_device) + for name, component in pipe.components.items(): + if name in components_to_quantize: + self.assertTrue(getattr(component.config, "quantization_config", None) is not None) + quantization_config = component.config.quantization_config + + if name == "text_encoder_2": + self.assertTrue(quantization_config.load_in_4bit) + self.assertTrue(quantization_config.quant_method == "bitsandbytes") + else: + self.assertTrue(quantization_config.quant_method == "quanto") + + _ = pipe(self.prompt, num_inference_steps=self.num_inference_steps) + + def test_raises_error_for_invalid_config(self): + with self.assertRaises(ValueError) as err_context: + _ = PipelineQuantizationConfig( + quant_mapping={ + "transformer": QuantoConfig(weights_dtype="int8"), + "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16), + }, + quant_backend="bitsandbytes_4bit", + ) + + self.assertTrue( + str(err_context.exception) + == "Both `quant_backend` and `quant_mapping` cannot be specified at the same time." + ) + + def test_validation_for_kwargs(self): + components_to_quantize = ["transformer", "text_encoder_2"] + with self.assertRaises(ValueError) as err_context: + _ = PipelineQuantizationConfig( + quant_backend="quanto", + quant_kwargs={"weights_dtype": "int8"}, + components_to_quantize=components_to_quantize, + ) + + self.assertTrue( + "The signatures of the __init__ methods of the quantization config classes" in str(err_context.exception) + ) + + def test_raises_error_for_wrong_config_class(self): + quant_config = { + "transformer": QuantoConfig(weights_dtype="int8"), + "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16), + } + with self.assertRaises(ValueError) as err_context: + _ = DiffusionPipeline.from_pretrained( + self.model_name, + quantization_config=quant_config, + torch_dtype=torch.bfloat16, + ) + self.assertTrue( + str(err_context.exception) == "`quantization_config` must be an instance of `PipelineQuantizationConfig`." + ) + + def test_validation_for_mapping(self): + with self.assertRaises(ValueError) as err_context: + _ = PipelineQuantizationConfig( + quant_mapping={ + "transformer": DiffusionPipeline(), + "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16), + } + ) + + self.assertTrue("Provided config for module_name=transformer could not be found" in str(err_context.exception)) + + def test_saving_loading(self): + quant_config = PipelineQuantizationConfig( + quant_mapping={ + "transformer": QuantoConfig(weights_dtype="int8"), + "text_encoder_2": TranBitsAndBytesConfig(load_in_4bit=True, compute_dtype=torch.bfloat16), + } + ) + components_to_quantize = list(quant_config.quant_mapping.keys()) + pipe = DiffusionPipeline.from_pretrained( + self.model_name, + quantization_config=quant_config, + torch_dtype=torch.bfloat16, + ).to(torch_device) + + pipe_inputs = {"prompt": self.prompt, "num_inference_steps": self.num_inference_steps, "output_type": "latent"} + output_1 = pipe(**pipe_inputs, generator=torch.manual_seed(self.seed)).images + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + loaded_pipe = DiffusionPipeline.from_pretrained(tmpdir, torch_dtype=torch.bfloat16).to(torch_device) + for name, component in loaded_pipe.components.items(): + if name in components_to_quantize: + self.assertTrue(getattr(component.config, "quantization_config", None) is not None) + quantization_config = component.config.quantization_config + + if name == "text_encoder_2": + self.assertTrue(quantization_config.load_in_4bit) + self.assertTrue(quantization_config.quant_method == "bitsandbytes") + else: + self.assertTrue(quantization_config.quant_method == "quanto") + + output_2 = loaded_pipe(**pipe_inputs, generator=torch.manual_seed(self.seed)).images + + self.assertTrue(torch.allclose(output_1, output_2)) + + @parameterized.expand(["quant_kwargs", "quant_mapping"]) + def test_warn_invalid_component(self, method): + invalid_component = "foo" + if method == "quant_kwargs": + components_to_quantize = ["transformer", invalid_component] + quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_8bit", + quant_kwargs={"load_in_8bit": True}, + components_to_quantize=components_to_quantize, + ) + else: + quant_config = PipelineQuantizationConfig( + quant_mapping={ + "transformer": QuantoConfig("int8"), + invalid_component: TranBitsAndBytesConfig(load_in_8bit=True), + } + ) + + logger = logging.get_logger("diffusers.pipelines.pipeline_loading_utils") + logger.setLevel(logging.WARNING) + with CaptureLogger(logger) as cap_logger: + _ = DiffusionPipeline.from_pretrained( + self.model_name, + quantization_config=quant_config, + torch_dtype=torch.bfloat16, + ) + self.assertTrue(invalid_component in cap_logger.out) + + @parameterized.expand(["quant_kwargs", "quant_mapping"]) + def test_no_quantization_for_all_invalid_components(self, method): + invalid_component = "foo" + if method == "quant_kwargs": + components_to_quantize = [invalid_component] + quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_8bit", + quant_kwargs={"load_in_8bit": True}, + components_to_quantize=components_to_quantize, + ) + else: + quant_config = PipelineQuantizationConfig( + quant_mapping={invalid_component: TranBitsAndBytesConfig(load_in_8bit=True)} + ) + + pipe = DiffusionPipeline.from_pretrained( + self.model_name, + quantization_config=quant_config, + torch_dtype=torch.bfloat16, + ) + for name, component in pipe.components.items(): + if isinstance(component, torch.nn.Module): + self.assertTrue(not hasattr(component.config, "quantization_config")) + + @parameterized.expand(["quant_kwargs", "quant_mapping"]) + def test_quant_config_repr(self, method): + component_name = "transformer" + if method == "quant_kwargs": + components_to_quantize = [component_name] + quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_8bit", + quant_kwargs={"load_in_8bit": True}, + components_to_quantize=components_to_quantize, + ) + else: + quant_config = PipelineQuantizationConfig( + quant_mapping={component_name: BitsAndBytesConfig(load_in_8bit=True)} + ) + + pipe = DiffusionPipeline.from_pretrained( + self.model_name, + quantization_config=quant_config, + torch_dtype=torch.bfloat16, + ) + self.assertTrue(getattr(pipe, "quantization_config", None) is not None) + retrieved_config = pipe.quantization_config + expected_config = """ +transformer BitsAndBytesConfig { + "_load_in_4bit": false, + "_load_in_8bit": true, + "bnb_4bit_compute_dtype": "float32", + "bnb_4bit_quant_storage": "uint8", + "bnb_4bit_quant_type": "fp4", + "bnb_4bit_use_double_quant": false, + "llm_int8_enable_fp32_cpu_offload": false, + "llm_int8_has_fp16_weight": false, + "llm_int8_skip_modules": null, + "llm_int8_threshold": 6.0, + "load_in_4bit": false, + "load_in_8bit": true, + "quant_method": "bitsandbytes" +} + +""" + expected_data = self._parse_config_string(expected_config) + actual_data = self._parse_config_string(str(retrieved_config)) + self.assertTrue(actual_data == expected_data) + + def _parse_config_string(self, config_string: str) -> tuple[str, dict]: + first_brace = config_string.find("{") + if first_brace == -1: + raise ValueError("Could not find opening brace '{' in the string.") + + json_part = config_string[first_brace:] + data = json.loads(json_part) + + return data + + def test_single_component_to_quantize(self): + component_to_quantize = "transformer" + quant_config = PipelineQuantizationConfig( + quant_backend="bitsandbytes_8bit", + quant_kwargs={"load_in_8bit": True}, + components_to_quantize=component_to_quantize, + ) + pipe = DiffusionPipeline.from_pretrained( + self.model_name, + quantization_config=quant_config, + torch_dtype=torch.bfloat16, + ) + for name, component in pipe.components.items(): + if name == component_to_quantize: + self.assertTrue(hasattr(component.config, "quantization_config")) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/test_torch_compile_utils.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/test_torch_compile_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..29758cbdd7352fec06290df2703eff78ee9bf92a --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/test_torch_compile_utils.py @@ -0,0 +1,106 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a clone of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import gc +import inspect + +import torch + +from diffusers import DiffusionPipeline + +from ..testing_utils import backend_empty_cache, require_torch_accelerator, slow, torch_device + + +@require_torch_accelerator +@slow +class QuantCompileTests: + @property + def quantization_config(self): + raise NotImplementedError( + "This property should be implemented in the subclass to return the appropriate quantization config." + ) + + def setUp(self): + super().setUp() + gc.collect() + backend_empty_cache(torch_device) + torch.compiler.reset() + + def tearDown(self): + super().tearDown() + gc.collect() + backend_empty_cache(torch_device) + torch.compiler.reset() + + def _init_pipeline(self, quantization_config, torch_dtype): + pipe = DiffusionPipeline.from_pretrained( + "stabilityai/stable-diffusion-3-medium-diffusers", + quantization_config=quantization_config, + torch_dtype=torch_dtype, + ) + return pipe + + def _test_torch_compile(self, torch_dtype=torch.bfloat16): + pipe = self._init_pipeline(self.quantization_config, torch_dtype).to(torch_device) + # `fullgraph=True` ensures no graph breaks + pipe.transformer.compile(fullgraph=True) + + # small resolutions to ensure speedy execution. + with torch._dynamo.config.patch(error_on_recompile=True): + pipe("a dog", num_inference_steps=2, max_sequence_length=16, height=256, width=256) + + def _test_torch_compile_with_cpu_offload(self, torch_dtype=torch.bfloat16): + pipe = self._init_pipeline(self.quantization_config, torch_dtype) + pipe.enable_model_cpu_offload() + # regional compilation is better for offloading. + # see: https://pytorch.org/blog/torch-compile-and-diffusers-a-hands-on-guide-to-peak-performance/ + if getattr(pipe.transformer, "_repeated_blocks"): + pipe.transformer.compile_repeated_blocks(fullgraph=True) + else: + pipe.transformer.compile() + + # small resolutions to ensure speedy execution. + pipe("a dog", num_inference_steps=2, max_sequence_length=16, height=256, width=256) + + def _test_torch_compile_with_group_offload_leaf(self, torch_dtype=torch.bfloat16, *, use_stream: bool = False): + torch._dynamo.config.cache_size_limit = 1000 + + pipe = self._init_pipeline(self.quantization_config, torch_dtype) + group_offload_kwargs = { + "onload_device": torch.device(torch_device), + "offload_device": torch.device("cpu"), + "offload_type": "leaf_level", + "use_stream": use_stream, + } + pipe.transformer.enable_group_offload(**group_offload_kwargs) + pipe.transformer.compile() + for name, component in pipe.components.items(): + if name != "transformer" and isinstance(component, torch.nn.Module): + if torch.device(component.device).type == "cpu": + component.to(torch_device) + + # small resolutions to ensure speedy execution. + pipe("a dog", num_inference_steps=2, max_sequence_length=16, height=256, width=256) + + def test_torch_compile(self): + self._test_torch_compile() + + def test_torch_compile_with_cpu_offload(self): + self._test_torch_compile_with_cpu_offload() + + def test_torch_compile_with_group_offload_leaf(self, use_stream=False): + for cls in inspect.getmro(self.__class__): + if "test_torch_compile_with_group_offload_leaf" in cls.__dict__ and cls is not QuantCompileTests: + return + self._test_torch_compile_with_group_offload_leaf(use_stream=use_stream) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/torchao/README.md b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/torchao/README.md new file mode 100644 index 0000000000000000000000000000000000000000..373593091ac0b3ade6be26dbcb830dd1e53e541a --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/torchao/README.md @@ -0,0 +1,53 @@ +The tests here are adapted from [`transformers` tests](https://github.com/huggingface/transformers/blob/3a8eb74668e9c2cc563b2f5c62fac174797063e0/tests/quantization/torchao_integration/). + +The benchmarks were run on a single H100. Below is `nvidia-smi`: + +```bash ++---------------------------------------------------------------------------------------+ +| NVIDIA-SMI 535.104.12 Driver Version: 535.104.12 CUDA Version: 12.2 | +|-----------------------------------------+----------------------+----------------------+ +| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | +| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | +| | | MIG M. | +|=========================================+======================+======================| +| 0 NVIDIA H100 80GB HBM3 On | 00000000:53:00.0 Off | 0 | +| N/A 34C P0 69W / 700W | 2MiB / 81559MiB | 0% Default | +| | | Disabled | ++-----------------------------------------+----------------------+----------------------+ + ++---------------------------------------------------------------------------------------+ +| Processes: | +| GPU GI CI PID Type Process name GPU Memory | +| ID ID Usage | +|=======================================================================================| +| No running processes found | ++---------------------------------------------------------------------------------------+ +``` + +The benchmark results for Flux and CogVideoX can be found in [this](https://github.com/huggingface/diffusers/pull/10009) PR. + +The tests, and the expected slices, were obtained from the `aws-g6e-xlarge-plus` GPU test runners. To run the slow tests, use the following command or an equivalent: + +```bash +HF_XET_HIGH_PERFORMANCE=1 RUN_SLOW=1 pytest -s tests/quantization/torchao/test_torchao.py::SlowTorchAoTests +``` + +`diffusers-cli`: + +```bash +- 🤗 Diffusers version: 0.32.0.dev0 +- Platform: Linux-5.15.0-1049-aws-x86_64-with-glibc2.31 +- Running on Google Colab?: No +- Python version: 3.10.14 +- PyTorch version (GPU?): 2.6.0.dev20241112+cu121 (False) +- Flax version (CPU?/GPU?/TPU?): not installed (NA) +- Jax version: not installed +- JaxLib version: not installed +- Huggingface_hub version: 0.26.2 +- Transformers version: 4.46.3 +- Accelerate version: 1.1.1 +- PEFT version: not installed +- Bitsandbytes version: not installed +- Safetensors version: 0.4.5 +- xFormers version: not installed +``` diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/torchao/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/torchao/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/torchao/test_torchao.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/torchao/test_torchao.py new file mode 100644 index 0000000000000000000000000000000000000000..a722eaece4d1635129d11544a4e7051d448ac664 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/torchao/test_torchao.py @@ -0,0 +1,961 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import importlib.metadata +import tempfile +import unittest +from typing import List + +import numpy as np +from packaging import version +from parameterized import parameterized +from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, T5EncoderModel + +from diffusers import ( + AutoencoderKL, + FlowMatchEulerDiscreteScheduler, + FluxPipeline, + FluxTransformer2DModel, + TorchAoConfig, +) +from diffusers.models.attention_processor import Attention +from diffusers.quantizers import PipelineQuantizationConfig + +from ...testing_utils import ( + Expectations, + backend_empty_cache, + backend_synchronize, + enable_full_determinism, + is_torch_available, + is_torchao_available, + nightly, + numpy_cosine_similarity_distance, + require_torch, + require_torch_accelerator, + require_torchao_version_greater_or_equal, + slow, + torch_device, +) +from ..test_torch_compile_utils import QuantCompileTests + + +enable_full_determinism() + + +if is_torch_available(): + import torch + import torch.nn as nn + + from ..utils import LoRALayer, get_memory_consumption_stat + + +if is_torchao_available(): + from torchao.dtypes import AffineQuantizedTensor + from torchao.quantization.linear_activation_quantized_tensor import LinearActivationQuantizedTensor + from torchao.quantization.quant_primitives import MappingType + from torchao.utils import get_model_size_in_bytes + + if version.parse(importlib.metadata.version("torchao")) >= version.Version("0.9.0"): + from torchao.quantization import Int8WeightOnlyConfig + + +@require_torch +@require_torch_accelerator +@require_torchao_version_greater_or_equal("0.14.0") +class TorchAoConfigTest(unittest.TestCase): + def test_to_dict(self): + """ + Makes sure the config format is properly set + """ + quantization_config = TorchAoConfig("int4_weight_only") + torchao_orig_config = quantization_config.to_dict() + + for key in torchao_orig_config: + self.assertEqual(getattr(quantization_config, key), torchao_orig_config[key]) + + def test_post_init_check(self): + """ + Test kwargs validations in TorchAoConfig + """ + _ = TorchAoConfig("int4_weight_only") + with self.assertRaisesRegex(ValueError, "is not supported"): + _ = TorchAoConfig("uint8") + + with self.assertRaisesRegex(ValueError, "does not support the following keyword arguments"): + _ = TorchAoConfig("int4_weight_only", group_size1=32) + + def test_repr(self): + """ + Check that there is no error in the repr + """ + quantization_config = TorchAoConfig("int4_weight_only", modules_to_not_convert=["conv"], group_size=8) + expected_repr = """TorchAoConfig { + "modules_to_not_convert": [ + "conv" + ], + "quant_method": "torchao", + "quant_type": "int4_weight_only", + "quant_type_kwargs": { + "group_size": 8 + } + }""".replace(" ", "").replace("\n", "") + quantization_repr = repr(quantization_config).replace(" ", "").replace("\n", "") + self.assertEqual(quantization_repr, expected_repr) + + quantization_config = TorchAoConfig("int4dq", group_size=64, act_mapping_type=MappingType.SYMMETRIC) + expected_repr = """TorchAoConfig { + "modules_to_not_convert": null, + "quant_method": "torchao", + "quant_type": "int4dq", + "quant_type_kwargs": { + "act_mapping_type": "SYMMETRIC", + "group_size": 64 + } + }""".replace(" ", "").replace("\n", "") + quantization_repr = repr(quantization_config).replace(" ", "").replace("\n", "") + self.assertEqual(quantization_repr, expected_repr) + + +# Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners +@require_torch +@require_torch_accelerator +@require_torchao_version_greater_or_equal("0.14.0") +class TorchAoTest(unittest.TestCase): + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_dummy_components( + self, quantization_config: TorchAoConfig, model_id: str = "hf-internal-testing/tiny-flux-pipe" + ): + transformer = FluxTransformer2DModel.from_pretrained( + model_id, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + ) + text_encoder = CLIPTextModel.from_pretrained(model_id, subfolder="text_encoder", torch_dtype=torch.bfloat16) + text_encoder_2 = T5EncoderModel.from_pretrained( + model_id, subfolder="text_encoder_2", torch_dtype=torch.bfloat16 + ) + tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer") + tokenizer_2 = AutoTokenizer.from_pretrained(model_id, subfolder="tokenizer_2") + vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.bfloat16) + scheduler = FlowMatchEulerDiscreteScheduler() + + return { + "scheduler": scheduler, + "text_encoder": text_encoder, + "text_encoder_2": text_encoder_2, + "tokenizer": tokenizer, + "tokenizer_2": tokenizer_2, + "transformer": transformer, + "vae": vae, + } + + def get_dummy_inputs(self, device: torch.device, seed: int = 0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator().manual_seed(seed) + + inputs = { + "prompt": "an astronaut riding a horse in space", + "height": 32, + "width": 32, + "num_inference_steps": 2, + "output_type": "np", + "generator": generator, + } + + return inputs + + def get_dummy_tensor_inputs(self, device=None, seed: int = 0): + batch_size = 1 + num_latent_channels = 4 + num_image_channels = 3 + height = width = 4 + sequence_length = 48 + embedding_dim = 32 + + torch.manual_seed(seed) + hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to( + device, dtype=torch.bfloat16 + ) + + torch.manual_seed(seed) + pooled_prompt_embeds = torch.randn((batch_size, embedding_dim)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + text_ids = torch.randn((sequence_length, num_image_channels)).to(device, dtype=torch.bfloat16) + + torch.manual_seed(seed) + image_ids = torch.randn((height * width, num_image_channels)).to(device, dtype=torch.bfloat16) + + timestep = torch.tensor([1.0]).to(device, dtype=torch.bfloat16).expand(batch_size) + + return { + "hidden_states": hidden_states, + "encoder_hidden_states": encoder_hidden_states, + "pooled_projections": pooled_prompt_embeds, + "txt_ids": text_ids, + "img_ids": image_ids, + "timestep": timestep, + } + + def _test_quant_type(self, quantization_config: TorchAoConfig, expected_slice: List[float], model_id: str): + components = self.get_dummy_components(quantization_config, model_id) + pipe = FluxPipeline(**components) + pipe.to(device=torch_device) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0] + output_slice = output[-1, -1, -3:, -3:].flatten() + + self.assertTrue(np.allclose(output_slice, expected_slice, atol=1e-3, rtol=1e-3)) + + def test_quantization(self): + for model_id in ["hf-internal-testing/tiny-flux-pipe", "hf-internal-testing/tiny-flux-sharded"]: + # fmt: off + QUANTIZATION_TYPES_TO_TEST = [ + ("int4wo", np.array([0.4648, 0.5234, 0.5547, 0.4219, 0.4414, 0.6445, 0.4336, 0.4531, 0.5625])), + ("int4dq", np.array([0.4688, 0.5195, 0.5547, 0.418, 0.4414, 0.6406, 0.4336, 0.4531, 0.5625])), + ("int8wo", np.array([0.4648, 0.5195, 0.5547, 0.4199, 0.4414, 0.6445, 0.4316, 0.4531, 0.5625])), + ("int8dq", np.array([0.4648, 0.5195, 0.5547, 0.4199, 0.4414, 0.6445, 0.4316, 0.4531, 0.5625])), + ("uint4wo", np.array([0.4609, 0.5234, 0.5508, 0.4199, 0.4336, 0.6406, 0.4316, 0.4531, 0.5625])), + ("uint7wo", np.array([0.4648, 0.5195, 0.5547, 0.4219, 0.4414, 0.6445, 0.4316, 0.4531, 0.5625])), + ] + + if TorchAoConfig._is_xpu_or_cuda_capability_atleast_8_9(): + QUANTIZATION_TYPES_TO_TEST.extend([ + ("float8wo_e5m2", np.array([0.4590, 0.5273, 0.5547, 0.4219, 0.4375, 0.6406, 0.4316, 0.4512, 0.5625])), + ("float8wo_e4m3", np.array([0.4648, 0.5234, 0.5547, 0.4219, 0.4414, 0.6406, 0.4316, 0.4531, 0.5625])), + # ===== + # The following lead to an internal torch error: + # RuntimeError: mat2 shape (32x4 must be divisible by 16 + # Skip these for now; TODO(aryan): investigate later + # ("float8dq_e4m3", np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])), + # ("float8dq_e4m3_tensor", np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])), + # ===== + # Cutlass fails to initialize for below + # ("float8dq_e4m3_row", np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])), + # ===== + ]) + if version.parse(importlib.metadata.version("torchao")) <= version.Version("0.14.1"): + QUANTIZATION_TYPES_TO_TEST.extend([ + ("fp4", np.array([0.4668, 0.5195, 0.5547, 0.4199, 0.4434, 0.6445, 0.4316, 0.4531, 0.5625])), + ("fp6", np.array([0.4668, 0.5195, 0.5547, 0.4199, 0.4434, 0.6445, 0.4316, 0.4531, 0.5625])), + ]) + # fmt: on + + for quantization_name, expected_slice in QUANTIZATION_TYPES_TO_TEST: + quant_kwargs = {} + if quantization_name in ["uint4wo", "uint7wo"]: + # The dummy flux model that we use has smaller dimensions. This imposes some restrictions on group_size here + quant_kwargs.update({"group_size": 16}) + quantization_config = TorchAoConfig( + quant_type=quantization_name, modules_to_not_convert=["x_embedder"], **quant_kwargs + ) + self._test_quant_type(quantization_config, expected_slice, model_id) + + @unittest.skip("Skipping floatx quantization tests") + def test_floatx_quantization(self): + for model_id in ["hf-internal-testing/tiny-flux-pipe", "hf-internal-testing/tiny-flux-sharded"]: + if TorchAoConfig._is_xpu_or_cuda_capability_atleast_8_9(): + if version.parse(importlib.metadata.version("torchao")) <= version.Version("0.14.1"): + quantization_config = TorchAoConfig(quant_type="fp4", modules_to_not_convert=["x_embedder"]) + self._test_quant_type( + quantization_config, + np.array( + [ + 0.4648, + 0.5195, + 0.5547, + 0.4180, + 0.4434, + 0.6445, + 0.4316, + 0.4531, + 0.5625, + ] + ), + model_id, + ) + else: + # Make sure the correct error is thrown + with self.assertRaisesRegex(ValueError, "Please downgrade"): + quantization_config = TorchAoConfig(quant_type="fp4", modules_to_not_convert=["x_embedder"]) + + def test_int4wo_quant_bfloat16_conversion(self): + """ + Tests whether the dtype of model will be modified to bfloat16 for int4 weight-only quantization. + """ + quantization_config = TorchAoConfig("int4_weight_only", group_size=64) + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + device_map=f"{torch_device}:0", + ) + + weight = quantized_model.transformer_blocks[0].ff.net[2].weight + self.assertTrue(isinstance(weight, AffineQuantizedTensor)) + self.assertEqual(weight.quant_min, 0) + self.assertEqual(weight.quant_max, 15) + + def test_device_map(self): + """ + Test if the quantized model int4 weight-only is working properly with "auto" and custom device maps. + The custom device map performs cpu/disk offloading as well. Also verifies that the device map is + correctly set (in the `hf_device_map` attribute of the model). + """ + custom_device_map_dict = { + "time_text_embed": torch_device, + "context_embedder": torch_device, + "x_embedder": torch_device, + "transformer_blocks.0": "cpu", + "single_transformer_blocks.0": "disk", + "norm_out": torch_device, + "proj_out": "cpu", + } + device_maps = ["auto", custom_device_map_dict] + + inputs = self.get_dummy_tensor_inputs(torch_device) + # requires with different expected slices since models are different due to offload (we don't quantize modules offloaded to cpu/disk) + expected_slice_auto = np.array( + [ + 0.34179688, + -0.03613281, + 0.01428223, + -0.22949219, + -0.49609375, + 0.4375, + -0.1640625, + -0.66015625, + 0.43164062, + ] + ) + expected_slice_offload = np.array( + [0.34375, -0.03515625, 0.0123291, -0.22753906, -0.49414062, 0.4375, -0.16308594, -0.66015625, 0.43554688] + ) + for device_map in device_maps: + if device_map == "auto": + expected_slice = expected_slice_auto + else: + expected_slice = expected_slice_offload + with tempfile.TemporaryDirectory() as offload_folder: + quantization_config = TorchAoConfig("int4_weight_only", group_size=64) + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + subfolder="transformer", + quantization_config=quantization_config, + device_map=device_map, + torch_dtype=torch.bfloat16, + offload_folder=offload_folder, + ) + + weight = quantized_model.transformer_blocks[0].ff.net[2].weight + + # Note that when performing cpu/disk offload, the offloaded weights are not quantized, only the weights on the gpu. + # This is not the case when the model are already quantized + if "transformer_blocks.0" in device_map: + self.assertTrue(isinstance(weight, nn.Parameter)) + else: + self.assertTrue(isinstance(weight, AffineQuantizedTensor)) + + output = quantized_model(**inputs)[0] + output_slice = output.flatten()[-9:].detach().float().cpu().numpy() + self.assertTrue(numpy_cosine_similarity_distance(output_slice, expected_slice) < 2e-3) + + with tempfile.TemporaryDirectory() as offload_folder: + quantization_config = TorchAoConfig("int4_weight_only", group_size=64) + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-sharded", + subfolder="transformer", + quantization_config=quantization_config, + device_map=device_map, + torch_dtype=torch.bfloat16, + offload_folder=offload_folder, + ) + + weight = quantized_model.transformer_blocks[0].ff.net[2].weight + if "transformer_blocks.0" in device_map: + self.assertTrue(isinstance(weight, nn.Parameter)) + else: + self.assertTrue(isinstance(weight, AffineQuantizedTensor)) + + output = quantized_model(**inputs)[0] + output_slice = output.flatten()[-9:].detach().float().cpu().numpy() + self.assertTrue(numpy_cosine_similarity_distance(output_slice, expected_slice) < 2e-3) + + def test_modules_to_not_convert(self): + quantization_config = TorchAoConfig("int8_weight_only", modules_to_not_convert=["transformer_blocks.0"]) + quantized_model_with_not_convert = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + ) + + unquantized_layer = quantized_model_with_not_convert.transformer_blocks[0].ff.net[2] + self.assertTrue(isinstance(unquantized_layer, torch.nn.Linear)) + self.assertFalse(isinstance(unquantized_layer.weight, AffineQuantizedTensor)) + self.assertEqual(unquantized_layer.weight.dtype, torch.bfloat16) + + quantized_layer = quantized_model_with_not_convert.proj_out + self.assertTrue(isinstance(quantized_layer.weight, AffineQuantizedTensor)) + + quantization_config = TorchAoConfig("int8_weight_only") + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + ) + + size_quantized_with_not_convert = get_model_size_in_bytes(quantized_model_with_not_convert) + size_quantized = get_model_size_in_bytes(quantized_model) + + self.assertTrue(size_quantized < size_quantized_with_not_convert) + + def test_training(self): + quantization_config = TorchAoConfig("int8_weight_only") + quantized_model = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/tiny-flux-pipe", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + ).to(torch_device) + + for param in quantized_model.parameters(): + # freeze the model as only adapter layers will be trained + param.requires_grad = False + if param.ndim == 1: + param.data = param.data.to(torch.float32) + + for _, module in quantized_model.named_modules(): + if isinstance(module, Attention): + module.to_q = LoRALayer(module.to_q, rank=4) + module.to_k = LoRALayer(module.to_k, rank=4) + module.to_v = LoRALayer(module.to_v, rank=4) + + with torch.amp.autocast(str(torch_device), dtype=torch.bfloat16): + inputs = self.get_dummy_tensor_inputs(torch_device) + output = quantized_model(**inputs)[0] + output.norm().backward() + + for module in quantized_model.modules(): + if isinstance(module, LoRALayer): + self.assertTrue(module.adapter[1].weight.grad is not None) + self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0) + + @nightly + def test_torch_compile(self): + r"""Test that verifies if torch.compile works with torchao quantization.""" + for model_id in ["hf-internal-testing/tiny-flux-pipe", "hf-internal-testing/tiny-flux-sharded"]: + quantization_config = TorchAoConfig("int8_weight_only") + components = self.get_dummy_components(quantization_config, model_id=model_id) + pipe = FluxPipeline(**components) + pipe.to(device=torch_device) + + inputs = self.get_dummy_inputs(torch_device) + normal_output = pipe(**inputs)[0].flatten()[-32:] + + pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True, dynamic=False) + inputs = self.get_dummy_inputs(torch_device) + compile_output = pipe(**inputs)[0].flatten()[-32:] + + # Note: Seems to require higher tolerance + self.assertTrue(np.allclose(normal_output, compile_output, atol=1e-2, rtol=1e-3)) + + def test_memory_footprint(self): + r""" + A simple test to check if the model conversion has been done correctly by checking on the + memory footprint of the converted model and the class type of the linear layers of the converted models + """ + for model_id in ["hf-internal-testing/tiny-flux-pipe", "hf-internal-testing/tiny-flux-sharded"]: + transformer_int4wo = self.get_dummy_components(TorchAoConfig("int4wo"), model_id=model_id)["transformer"] + transformer_int4wo_gs32 = self.get_dummy_components( + TorchAoConfig("int4wo", group_size=32), model_id=model_id + )["transformer"] + transformer_int8wo = self.get_dummy_components(TorchAoConfig("int8wo"), model_id=model_id)["transformer"] + transformer_bf16 = self.get_dummy_components(None, model_id=model_id)["transformer"] + + # Will not quantized all the layers by default due to the model weights shapes not being divisible by group_size=64 + for block in transformer_int4wo.transformer_blocks: + self.assertTrue(isinstance(block.ff.net[2].weight, AffineQuantizedTensor)) + self.assertTrue(isinstance(block.ff_context.net[2].weight, AffineQuantizedTensor)) + + # Will quantize all the linear layers except x_embedder + for name, module in transformer_int4wo_gs32.named_modules(): + if isinstance(module, nn.Linear) and name not in ["x_embedder"]: + self.assertTrue(isinstance(module.weight, AffineQuantizedTensor)) + + # Will quantize all the linear layers + for module in transformer_int8wo.modules(): + if isinstance(module, nn.Linear): + self.assertTrue(isinstance(module.weight, AffineQuantizedTensor)) + + total_int4wo = get_model_size_in_bytes(transformer_int4wo) + total_int4wo_gs32 = get_model_size_in_bytes(transformer_int4wo_gs32) + total_int8wo = get_model_size_in_bytes(transformer_int8wo) + total_bf16 = get_model_size_in_bytes(transformer_bf16) + + # TODO: refactor to align with other quantization tests + # Latter has smaller group size, so more groups -> more scales and zero points + self.assertTrue(total_int4wo < total_int4wo_gs32) + # int8 quantizes more layers compare to int4 with default group size + self.assertTrue(total_int8wo < total_int4wo) + # int4wo does not quantize too many layers because of default group size, but for the layers it does + # there is additional overhead of scales and zero points + self.assertTrue(total_bf16 < total_int4wo) + + def test_model_memory_usage(self): + model_id = "hf-internal-testing/tiny-flux-pipe" + expected_memory_saving_ratios = Expectations( + { + # XPU: For this tiny model, per-tensor overheads (alignment, fragmentation, metadata) become visible. + # While XPU doesn't have the large fixed cuBLAS workspace of A100, these small overheads prevent reaching the ideal 2.0 ratio. + # Observed ~1.27x (158k vs 124k) for model size. + # The runtime memory overhead is ~88k for both bf16 and int8wo. Adding this to model size: (158k+88k)/(124k+88k) ≈ 1.15. + ("xpu", None): 1.15, + # On Ampere, the cuBLAS kernels used for matrix multiplication often allocate a fixed-size workspace. + # Since the tiny-flux model weights are likely smaller than or comparable to this workspace, the total memory is dominated by the workspace. + ("cuda", 8): 1.02, + # On Hopper, TorchAO utilizes newer, highly optimized kernels (via Triton or CUTLASS 3.x) that are designed to be workspace-free or use negligible extra memory. + # Additionally, Triton kernels often handle unaligned memory better, avoiding the padding overhead seen on other backends for tiny tensors. + # This allows it to achieve the near-ideal 2.0x compression ratio. + ("cuda", 9): 2.0, + } + ) + expected_memory_saving_ratio = expected_memory_saving_ratios.get_expectation() + inputs = self.get_dummy_tensor_inputs(device=torch_device) + + transformer_bf16 = self.get_dummy_components(None, model_id=model_id)["transformer"] + transformer_bf16.to(torch_device) + unquantized_model_memory = get_memory_consumption_stat(transformer_bf16, inputs) + del transformer_bf16 + + transformer_int8wo = self.get_dummy_components(TorchAoConfig("int8wo"), model_id=model_id)["transformer"] + transformer_int8wo.to(torch_device) + quantized_model_memory = get_memory_consumption_stat(transformer_int8wo, inputs) + assert unquantized_model_memory / quantized_model_memory >= expected_memory_saving_ratio + + def test_wrong_config(self): + with self.assertRaises(ValueError): + self.get_dummy_components(TorchAoConfig("int42")) + + def test_sequential_cpu_offload(self): + r""" + A test that checks if inference runs as expected when sequential cpu offloading is enabled. + """ + quantization_config = TorchAoConfig("int8wo") + components = self.get_dummy_components(quantization_config) + pipe = FluxPipeline(**components) + pipe.enable_sequential_cpu_offload() + + inputs = self.get_dummy_inputs(torch_device) + _ = pipe(**inputs) + + @require_torchao_version_greater_or_equal("0.9.0") + def test_aobase_config(self): + quantization_config = TorchAoConfig(Int8WeightOnlyConfig()) + components = self.get_dummy_components(quantization_config) + pipe = FluxPipeline(**components).to(torch_device) + + inputs = self.get_dummy_inputs(torch_device) + _ = pipe(**inputs) + + +# Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners +@require_torch +@require_torch_accelerator +@require_torchao_version_greater_or_equal("0.14.0") +class TorchAoSerializationTest(unittest.TestCase): + model_name = "hf-internal-testing/tiny-flux-pipe" + + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_dummy_model(self, quant_method, quant_method_kwargs, device=None): + quantization_config = TorchAoConfig(quant_method, **quant_method_kwargs) + quantized_model = FluxTransformer2DModel.from_pretrained( + self.model_name, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + ) + return quantized_model.to(device) + + def get_dummy_tensor_inputs(self, device=None, seed: int = 0): + batch_size = 1 + num_latent_channels = 4 + num_image_channels = 3 + height = width = 4 + sequence_length = 48 + embedding_dim = 32 + + torch.manual_seed(seed) + hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to(device, dtype=torch.bfloat16) + encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to( + device, dtype=torch.bfloat16 + ) + pooled_prompt_embeds = torch.randn((batch_size, embedding_dim)).to(device, dtype=torch.bfloat16) + text_ids = torch.randn((sequence_length, num_image_channels)).to(device, dtype=torch.bfloat16) + image_ids = torch.randn((height * width, num_image_channels)).to(device, dtype=torch.bfloat16) + timestep = torch.tensor([1.0]).to(device, dtype=torch.bfloat16).expand(batch_size) + + return { + "hidden_states": hidden_states, + "encoder_hidden_states": encoder_hidden_states, + "pooled_projections": pooled_prompt_embeds, + "txt_ids": text_ids, + "img_ids": image_ids, + "timestep": timestep, + } + + def _test_original_model_expected_slice(self, quant_method, quant_method_kwargs, expected_slice): + quantized_model = self.get_dummy_model(quant_method, quant_method_kwargs, torch_device) + inputs = self.get_dummy_tensor_inputs(torch_device) + output = quantized_model(**inputs)[0] + output_slice = output.flatten()[-9:].detach().float().cpu().numpy() + weight = quantized_model.transformer_blocks[0].ff.net[2].weight + self.assertTrue(isinstance(weight, (AffineQuantizedTensor, LinearActivationQuantizedTensor))) + self.assertTrue(numpy_cosine_similarity_distance(output_slice, expected_slice) < 1e-3) + + def _check_serialization_expected_slice(self, quant_method, quant_method_kwargs, expected_slice, device): + quantized_model = self.get_dummy_model(quant_method, quant_method_kwargs, device) + + with tempfile.TemporaryDirectory() as tmp_dir: + quantized_model.save_pretrained(tmp_dir, safe_serialization=False) + loaded_quantized_model = FluxTransformer2DModel.from_pretrained( + tmp_dir, torch_dtype=torch.bfloat16, use_safetensors=False + ).to(device=torch_device) + + inputs = self.get_dummy_tensor_inputs(torch_device) + output = loaded_quantized_model(**inputs)[0] + + output_slice = output.flatten()[-9:].detach().float().cpu().numpy() + self.assertTrue( + isinstance( + loaded_quantized_model.proj_out.weight, (AffineQuantizedTensor, LinearActivationQuantizedTensor) + ) + ) + self.assertTrue(numpy_cosine_similarity_distance(output_slice, expected_slice) < 1e-3) + + def test_int_a8w8_accelerator(self): + quant_method, quant_method_kwargs = "int8_dynamic_activation_int8_weight", {} + expected_slice = np.array([0.3633, -0.1357, -0.0188, -0.249, -0.4688, 0.5078, -0.1289, -0.6914, 0.4551]) + device = torch_device + self._test_original_model_expected_slice(quant_method, quant_method_kwargs, expected_slice) + self._check_serialization_expected_slice(quant_method, quant_method_kwargs, expected_slice, device) + + def test_int_a16w8_accelerator(self): + quant_method, quant_method_kwargs = "int8_weight_only", {} + expected_slice = np.array([0.3613, -0.127, -0.0223, -0.2539, -0.459, 0.4961, -0.1357, -0.6992, 0.4551]) + device = torch_device + self._test_original_model_expected_slice(quant_method, quant_method_kwargs, expected_slice) + self._check_serialization_expected_slice(quant_method, quant_method_kwargs, expected_slice, device) + + def test_int_a8w8_cpu(self): + quant_method, quant_method_kwargs = "int8_dynamic_activation_int8_weight", {} + expected_slice = np.array([0.3633, -0.1357, -0.0188, -0.249, -0.4688, 0.5078, -0.1289, -0.6914, 0.4551]) + device = "cpu" + self._test_original_model_expected_slice(quant_method, quant_method_kwargs, expected_slice) + self._check_serialization_expected_slice(quant_method, quant_method_kwargs, expected_slice, device) + + def test_int_a16w8_cpu(self): + quant_method, quant_method_kwargs = "int8_weight_only", {} + expected_slice = np.array([0.3613, -0.127, -0.0223, -0.2539, -0.459, 0.4961, -0.1357, -0.6992, 0.4551]) + device = "cpu" + self._test_original_model_expected_slice(quant_method, quant_method_kwargs, expected_slice) + self._check_serialization_expected_slice(quant_method, quant_method_kwargs, expected_slice, device) + + @require_torchao_version_greater_or_equal("0.9.0") + def test_aobase_config(self): + quant_method, quant_method_kwargs = Int8WeightOnlyConfig(), {} + expected_slice = np.array([0.3613, -0.127, -0.0223, -0.2539, -0.459, 0.4961, -0.1357, -0.6992, 0.4551]) + device = torch_device + self._test_original_model_expected_slice(quant_method, quant_method_kwargs, expected_slice) + self._check_serialization_expected_slice(quant_method, quant_method_kwargs, expected_slice, device) + + +@require_torchao_version_greater_or_equal("0.14.0") +class TorchAoCompileTest(QuantCompileTests, unittest.TestCase): + @property + def quantization_config(self): + return PipelineQuantizationConfig( + quant_mapping={"transformer": TorchAoConfig(Int8WeightOnlyConfig())}, + ) + + def test_torch_compile_with_cpu_offload(self): + pipe = self._init_pipeline(self.quantization_config, torch.bfloat16) + pipe.enable_model_cpu_offload() + # No compilation because it fails with: + # RuntimeError: _apply(): Couldn't swap Linear.weight + + # small resolutions to ensure speedy execution. + pipe("a dog", num_inference_steps=2, max_sequence_length=16, height=256, width=256) + + @parameterized.expand([False, True]) + @unittest.skip( + """ + For `use_stream=False`: + - Changing the device of AQT tensor, with `param.data = param.data.to(device)` as done in group offloading implementation + is unsupported in TorchAO. When compiling, FakeTensor device mismatch causes failure. + For `use_stream=True`: + Using non-default stream requires ability to pin tensors. AQT does not seem to support this yet in TorchAO. + """ + ) + def test_torch_compile_with_group_offload_leaf(self, use_stream): + # For use_stream=False: + # If we run group offloading without compilation, we will see: + # RuntimeError: Attempted to set the storage of a tensor on device "cpu" to a storage on different device "cuda:0". This is no longer allowed; the devices must match. + # When running with compilation, the error ends up being different: + # Dynamo failed to run FX node with fake tensors: call_function (*(FakeTensor(..., device='cuda:0', size=(s0, 256), dtype=torch.bfloat16), AffineQuantizedTensor(tensor_impl=PlainAQTTensorImpl(data=FakeTensor(..., size=(1536, 256), dtype=torch.int8)... , scale=FakeTensor(..., size=(1536,), dtype=torch.bfloat16)... , zero_point=FakeTensor(..., size=(1536,), dtype=torch.int64)... , _layout=PlainLayout()), block_size=(1, 256), shape=torch.Size([1536, 256]), device=cpu, dtype=torch.bfloat16, requires_grad=False), Parameter(FakeTensor(..., device='cuda:0', size=(1536,), dtype=torch.bfloat16, + # requires_grad=True))), **{}): got RuntimeError('Unhandled FakeTensor Device Propagation for aten.mm.default, found two different devices cuda:0, cpu') + # Looks like something that will have to be looked into upstream. + # for linear layers, weight.tensor_impl shows cuda... but: + # weight.tensor_impl.{data,scale,zero_point}.device will be cpu + + # For use_stream=True: + # NotImplementedError: AffineQuantizedTensor dispatch: attempting to run unimplemented operator/function: func=, types=(,), arg_types=(,), kwarg_types={} + super()._test_torch_compile_with_group_offload_leaf(use_stream=use_stream) + + +# Slices for these tests have been obtained on our aws-g6e-xlarge-plus runners +@require_torch +@require_torch_accelerator +@require_torchao_version_greater_or_equal("0.14.0") +@slow +@nightly +class SlowTorchAoTests(unittest.TestCase): + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_dummy_components(self, quantization_config: TorchAoConfig): + # This is just for convenience, so that we can modify it at one place for custom environments and locally testing + cache_dir = None + model_id = "black-forest-labs/FLUX.1-dev" + transformer = FluxTransformer2DModel.from_pretrained( + model_id, + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + cache_dir=cache_dir, + ) + text_encoder = CLIPTextModel.from_pretrained( + model_id, subfolder="text_encoder", torch_dtype=torch.bfloat16, cache_dir=cache_dir + ) + text_encoder_2 = T5EncoderModel.from_pretrained( + model_id, subfolder="text_encoder_2", torch_dtype=torch.bfloat16, cache_dir=cache_dir + ) + tokenizer = CLIPTokenizer.from_pretrained(model_id, subfolder="tokenizer", cache_dir=cache_dir) + tokenizer_2 = AutoTokenizer.from_pretrained(model_id, subfolder="tokenizer_2", cache_dir=cache_dir) + vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.bfloat16, cache_dir=cache_dir) + scheduler = FlowMatchEulerDiscreteScheduler() + + return { + "scheduler": scheduler, + "text_encoder": text_encoder, + "text_encoder_2": text_encoder_2, + "tokenizer": tokenizer, + "tokenizer_2": tokenizer_2, + "transformer": transformer, + "vae": vae, + } + + def get_dummy_inputs(self, device: torch.device, seed: int = 0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator().manual_seed(seed) + + inputs = { + "prompt": "an astronaut riding a horse in space", + "height": 512, + "width": 512, + "num_inference_steps": 20, + "output_type": "np", + "generator": generator, + } + + return inputs + + def _test_quant_type(self, quantization_config, expected_slice): + components = self.get_dummy_components(quantization_config) + pipe = FluxPipeline(**components) + pipe.enable_model_cpu_offload() + + weight = pipe.transformer.transformer_blocks[0].ff.net[2].weight + self.assertTrue(isinstance(weight, (AffineQuantizedTensor, LinearActivationQuantizedTensor))) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0].flatten() + output_slice = np.concatenate((output[:16], output[-16:])) + self.assertTrue(np.allclose(output_slice, expected_slice, atol=1e-3, rtol=1e-3)) + + def test_quantization(self): + # fmt: off + QUANTIZATION_TYPES_TO_TEST = [ + ("int8wo", np.array([0.0505, 0.0742, 0.1367, 0.0429, 0.0585, 0.1386, 0.0585, 0.0703, 0.1367, 0.0566, 0.0703, 0.1464, 0.0546, 0.0703, 0.1425, 0.0546, 0.3535, 0.7578, 0.5000, 0.4062, 0.7656, 0.5117, 0.4121, 0.7656, 0.5117, 0.3984, 0.7578, 0.5234, 0.4023, 0.7382, 0.5390, 0.4570])), + ("int8dq", np.array([0.0546, 0.0761, 0.1386, 0.0488, 0.0644, 0.1425, 0.0605, 0.0742, 0.1406, 0.0625, 0.0722, 0.1523, 0.0625, 0.0742, 0.1503, 0.0605, 0.3886, 0.7968, 0.5507, 0.4492, 0.7890, 0.5351, 0.4316, 0.8007, 0.5390, 0.4179, 0.8281, 0.5820, 0.4531, 0.7812, 0.5703, 0.4921])), + ] + + if TorchAoConfig._is_xpu_or_cuda_capability_atleast_8_9(): + QUANTIZATION_TYPES_TO_TEST.extend([ + ("float8wo_e4m3", np.array([0.0546, 0.0722, 0.1328, 0.0468, 0.0585, 0.1367, 0.0605, 0.0703, 0.1328, 0.0625, 0.0703, 0.1445, 0.0585, 0.0703, 0.1406, 0.0605, 0.3496, 0.7109, 0.4843, 0.4042, 0.7226, 0.5000, 0.4160, 0.7031, 0.4824, 0.3886, 0.6757, 0.4667, 0.3710, 0.6679, 0.4902, 0.4238])), + ]) + if version.parse(importlib.metadata.version("torchao")) <= version.Version("0.14.1"): + QUANTIZATION_TYPES_TO_TEST.extend([ + ("fp5_e3m1", np.array([0.0527, 0.0762, 0.1309, 0.0449, 0.0645, 0.1328, 0.0566, 0.0723, 0.125, 0.0566, 0.0703, 0.1328, 0.0566, 0.0742, 0.1348, 0.0566, 0.3633, 0.7617, 0.5273, 0.4277, 0.7891, 0.5469, 0.4375, 0.8008, 0.5586, 0.4336, 0.7383, 0.5156, 0.3906, 0.6992, 0.5156, 0.4375])), + ]) + # fmt: on + + for quantization_name, expected_slice in QUANTIZATION_TYPES_TO_TEST: + quantization_config = TorchAoConfig(quant_type=quantization_name, modules_to_not_convert=["x_embedder"]) + self._test_quant_type(quantization_config, expected_slice) + gc.collect() + backend_empty_cache(torch_device) + backend_synchronize(torch_device) + + def test_serialization_int8wo(self): + quantization_config = TorchAoConfig("int8wo") + components = self.get_dummy_components(quantization_config) + pipe = FluxPipeline(**components) + pipe.enable_model_cpu_offload() + + weight = pipe.transformer.x_embedder.weight + self.assertTrue(isinstance(weight, AffineQuantizedTensor)) + + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0].flatten()[:128] + + with tempfile.TemporaryDirectory() as tmp_dir: + pipe.transformer.save_pretrained(tmp_dir, safe_serialization=False) + pipe.remove_all_hooks() + del pipe.transformer + gc.collect() + backend_empty_cache(torch_device) + backend_synchronize(torch_device) + transformer = FluxTransformer2DModel.from_pretrained( + tmp_dir, torch_dtype=torch.bfloat16, use_safetensors=False + ) + pipe.transformer = transformer + pipe.enable_model_cpu_offload() + + weight = transformer.x_embedder.weight + self.assertTrue(isinstance(weight, AffineQuantizedTensor)) + + loaded_output = pipe(**inputs)[0].flatten()[:128] + # Seems to require higher tolerance depending on which machine it is being run. + # A difference of 0.06 in normalized pixel space (-1 to 1), corresponds to a difference of + # 0.06 / 2 * 255 = 7.65 in pixel space (0 to 255). On our CI runners, the difference is about 0.04, + # on DGX it is 0.06, and on audace it is 0.037. So, we are using a tolerance of 0.06 here. + self.assertTrue(np.allclose(output, loaded_output, atol=0.06)) + + def test_memory_footprint_int4wo(self): + # The original checkpoints are in bf16 and about 24 GB + expected_memory_in_gb = 6.0 + quantization_config = TorchAoConfig("int4wo") + cache_dir = None + transformer = FluxTransformer2DModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + cache_dir=cache_dir, + ) + int4wo_memory_in_gb = get_model_size_in_bytes(transformer) / 1024**3 + self.assertTrue(int4wo_memory_in_gb < expected_memory_in_gb) + + def test_memory_footprint_int8wo(self): + # The original checkpoints are in bf16 and about 24 GB + expected_memory_in_gb = 12.0 + quantization_config = TorchAoConfig("int8wo") + cache_dir = None + transformer = FluxTransformer2DModel.from_pretrained( + "black-forest-labs/FLUX.1-dev", + subfolder="transformer", + quantization_config=quantization_config, + torch_dtype=torch.bfloat16, + cache_dir=cache_dir, + ) + int8wo_memory_in_gb = get_model_size_in_bytes(transformer) / 1024**3 + self.assertTrue(int8wo_memory_in_gb < expected_memory_in_gb) + + +@require_torch +@require_torch_accelerator +@require_torchao_version_greater_or_equal("0.14.0") +@slow +@nightly +class SlowTorchAoPreserializedModelTests(unittest.TestCase): + def tearDown(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_dummy_inputs(self, device: torch.device, seed: int = 0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator().manual_seed(seed) + + inputs = { + "prompt": "an astronaut riding a horse in space", + "height": 512, + "width": 512, + "num_inference_steps": 20, + "output_type": "np", + "generator": generator, + } + + return inputs + + def test_transformer_int8wo(self): + # fmt: off + expected_slice = np.array([0.0566, 0.0781, 0.1426, 0.0488, 0.0684, 0.1504, 0.0625, 0.0781, 0.1445, 0.0625, 0.0781, 0.1562, 0.0547, 0.0723, 0.1484, 0.0566, 0.5703, 0.8867, 0.7266, 0.5742, 0.875, 0.7148, 0.5586, 0.875, 0.7148, 0.5547, 0.8633, 0.7109, 0.5469, 0.8398, 0.6992, 0.5703]) + # fmt: on + + # This is just for convenience, so that we can modify it at one place for custom environments and locally testing + cache_dir = None + transformer = FluxTransformer2DModel.from_pretrained( + "hf-internal-testing/FLUX.1-Dev-TorchAO-int8wo-transformer", + torch_dtype=torch.bfloat16, + use_safetensors=False, + cache_dir=cache_dir, + ) + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16, cache_dir=cache_dir + ) + pipe.enable_model_cpu_offload() + + # Verify that all linear layer weights are quantized + for name, module in pipe.transformer.named_modules(): + if isinstance(module, nn.Linear): + self.assertTrue(isinstance(module.weight, AffineQuantizedTensor)) + + # Verify outputs match expected slice + inputs = self.get_dummy_inputs(torch_device) + output = pipe(**inputs)[0].flatten() + output_slice = np.concatenate((output[:16], output[-16:])) + self.assertTrue(np.allclose(output_slice, expected_slice, atol=1e-3, rtol=1e-3)) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/utils.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a74ece5a3a3ad27de1abc004cdb2f34ec4c28287 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/quantization/utils.py @@ -0,0 +1,45 @@ +from diffusers.utils import is_torch_available + +from ..testing_utils import ( + backend_empty_cache, + backend_max_memory_allocated, + backend_reset_peak_memory_stats, + torch_device, +) + + +if is_torch_available(): + import torch + import torch.nn as nn + + class LoRALayer(nn.Module): + """Wraps a linear layer with LoRA-like adapter - Used for testing purposes only + + Taken from + https://github.com/huggingface/transformers/blob/566302686a71de14125717dea9a6a45b24d42b37/tests/quantization/bnb/test_4bit.py#L62C5-L78C77 + """ + + def __init__(self, module: nn.Module, rank: int): + super().__init__() + self.module = module + self.adapter = nn.Sequential( + nn.Linear(module.in_features, rank, bias=False), + nn.Linear(rank, module.out_features, bias=False), + ) + small_std = (2.0 / (5 * min(module.in_features, module.out_features))) ** 0.5 + nn.init.normal_(self.adapter[0].weight, std=small_std) + nn.init.zeros_(self.adapter[1].weight) + self.adapter.to(module.weight.device) + + def forward(self, input, *args, **kwargs): + return self.module(input, *args, **kwargs) + self.adapter(input) + + @torch.no_grad() + @torch.inference_mode() + def get_memory_consumption_stat(model, inputs): + backend_reset_peak_memory_stats(torch_device) + backend_empty_cache(torch_device) + + model(**inputs) + max_mem_allocated = backend_max_memory_allocated(torch_device) + return max_mem_allocated diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/remote/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/remote/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/remote/test_remote_decode.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/remote/test_remote_decode.py new file mode 100644 index 0000000000000000000000000000000000000000..e48ddcb1743898757d76e175cdb01b3514a3242a --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/remote/test_remote_decode.py @@ -0,0 +1,536 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +import PIL.Image +import torch + +from diffusers.image_processor import VaeImageProcessor +from diffusers.utils.constants import ( + DECODE_ENDPOINT_FLUX, + DECODE_ENDPOINT_HUNYUAN_VIDEO, + DECODE_ENDPOINT_SD_V1, + DECODE_ENDPOINT_SD_XL, +) +from diffusers.utils.remote_utils import ( + remote_decode, +) +from diffusers.video_processor import VideoProcessor + +from ..testing_utils import ( + enable_full_determinism, + slow, + torch_all_close, + torch_device, +) + + +enable_full_determinism() + + +class RemoteAutoencoderKLMixin: + shape: tuple[int, ...] = None + out_hw: tuple[int, int] = None + endpoint: str = None + dtype: torch.dtype = None + scaling_factor: float = None + shift_factor: float = None + processor_cls: VaeImageProcessor | VideoProcessor = None + output_pil_slice: torch.Tensor = None + output_pt_slice: torch.Tensor = None + partial_postprocess_return_pt_slice: torch.Tensor = None + return_pt_slice: torch.Tensor = None + width: int = None + height: int = None + + def get_dummy_inputs(self): + inputs = { + "endpoint": self.endpoint, + "tensor": torch.randn( + self.shape, + device=torch_device, + dtype=self.dtype, + generator=torch.Generator(torch_device).manual_seed(13), + ), + "scaling_factor": self.scaling_factor, + "shift_factor": self.shift_factor, + "height": self.height, + "width": self.width, + } + return inputs + + def test_no_scaling(self): + inputs = self.get_dummy_inputs() + if inputs["scaling_factor"] is not None: + inputs["tensor"] = inputs["tensor"] / inputs["scaling_factor"] + inputs["scaling_factor"] = None + if inputs["shift_factor"] is not None: + inputs["tensor"] = inputs["tensor"] + inputs["shift_factor"] + inputs["shift_factor"] = None + processor = self.processor_cls() + output = remote_decode( + output_type="pt", + # required for now, will be removed in next update + do_scaling=False, + processor=processor, + **inputs, + ) + assert isinstance(output, PIL.Image.Image) + self.assertTrue(isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}") + self.assertEqual(output.height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}") + self.assertEqual(output.width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}") + output_slice = torch.from_numpy(np.array(output)[0, -3:, -3:].flatten()) + # Increased tolerance for Flux Packed diff [1, 0, 1, 0, 0, 0, 0, 0, 0] + self.assertTrue( + torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1), + f"{output_slice}", + ) + + def test_output_type_pt(self): + inputs = self.get_dummy_inputs() + processor = self.processor_cls() + output = remote_decode(output_type="pt", processor=processor, **inputs) + assert isinstance(output, PIL.Image.Image) + self.assertTrue(isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}") + self.assertEqual(output.height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}") + self.assertEqual(output.width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}") + output_slice = torch.from_numpy(np.array(output)[0, -3:, -3:].flatten()) + self.assertTrue( + torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1e-2), f"{output_slice}" + ) + + # output is visually the same, slice is flaky? + def test_output_type_pil(self): + inputs = self.get_dummy_inputs() + output = remote_decode(output_type="pil", **inputs) + self.assertTrue(isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}") + self.assertEqual(output.height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}") + self.assertEqual(output.width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}") + + def test_output_type_pil_image_format(self): + inputs = self.get_dummy_inputs() + output = remote_decode(output_type="pil", image_format="png", **inputs) + self.assertTrue(isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}") + self.assertEqual(output.height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}") + self.assertEqual(output.width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}") + self.assertEqual(output.format, "png", f"Expected image format `png`, got {output.format}") + output_slice = torch.from_numpy(np.array(output)[0, -3:, -3:].flatten()) + self.assertTrue( + torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1e-2), f"{output_slice}" + ) + + def test_output_type_pt_partial_postprocess(self): + inputs = self.get_dummy_inputs() + output = remote_decode(output_type="pt", partial_postprocess=True, **inputs) + self.assertTrue(isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}") + self.assertEqual(output.height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}") + self.assertEqual(output.width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}") + output_slice = torch.from_numpy(np.array(output)[0, -3:, -3:].flatten()) + self.assertTrue( + torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1e-2), f"{output_slice}" + ) + + def test_output_type_pt_return_type_pt(self): + inputs = self.get_dummy_inputs() + output = remote_decode(output_type="pt", return_type="pt", **inputs) + self.assertTrue(isinstance(output, torch.Tensor), f"Expected `torch.Tensor` output, got {type(output)}") + self.assertEqual( + output.shape[2], self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.shape[2]}" + ) + self.assertEqual( + output.shape[3], self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.shape[3]}" + ) + output_slice = output[0, 0, -3:, -3:].flatten() + self.assertTrue( + torch_all_close(output_slice, self.return_pt_slice.to(output_slice.dtype), rtol=1e-3, atol=1e-3), + f"{output_slice}", + ) + + def test_output_type_pt_partial_postprocess_return_type_pt(self): + inputs = self.get_dummy_inputs() + output = remote_decode(output_type="pt", partial_postprocess=True, return_type="pt", **inputs) + self.assertTrue(isinstance(output, torch.Tensor), f"Expected `torch.Tensor` output, got {type(output)}") + self.assertEqual( + output.shape[1], self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.shape[1]}" + ) + self.assertEqual( + output.shape[2], self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.shape[2]}" + ) + output_slice = output[0, -3:, -3:, 0].flatten().cpu() + self.assertTrue( + torch_all_close(output_slice, self.partial_postprocess_return_pt_slice.to(output_slice.dtype), rtol=1e-2), + f"{output_slice}", + ) + + def test_do_scaling_deprecation(self): + inputs = self.get_dummy_inputs() + inputs.pop("scaling_factor", None) + inputs.pop("shift_factor", None) + with self.assertWarns(FutureWarning) as warning: + _ = remote_decode(output_type="pt", partial_postprocess=True, **inputs) + self.assertEqual( + str(warning.warnings[0].message), + "`do_scaling` is deprecated, pass `scaling_factor` and `shift_factor` if required.", + str(warning.warnings[0].message), + ) + + def test_input_tensor_type_base64_deprecation(self): + inputs = self.get_dummy_inputs() + with self.assertWarns(FutureWarning) as warning: + _ = remote_decode(output_type="pt", input_tensor_type="base64", partial_postprocess=True, **inputs) + self.assertEqual( + str(warning.warnings[0].message), + "input_tensor_type='base64' is deprecated. Using `binary`.", + str(warning.warnings[0].message), + ) + + def test_output_tensor_type_base64_deprecation(self): + inputs = self.get_dummy_inputs() + with self.assertWarns(FutureWarning) as warning: + _ = remote_decode(output_type="pt", output_tensor_type="base64", partial_postprocess=True, **inputs) + self.assertEqual( + str(warning.warnings[0].message), + "output_tensor_type='base64' is deprecated. Using `binary`.", + str(warning.warnings[0].message), + ) + + +class RemoteAutoencoderKLHunyuanVideoMixin(RemoteAutoencoderKLMixin): + def test_no_scaling(self): + inputs = self.get_dummy_inputs() + if inputs["scaling_factor"] is not None: + inputs["tensor"] = inputs["tensor"] / inputs["scaling_factor"] + inputs["scaling_factor"] = None + if inputs["shift_factor"] is not None: + inputs["tensor"] = inputs["tensor"] + inputs["shift_factor"] + inputs["shift_factor"] = None + processor = self.processor_cls() + output = remote_decode( + output_type="pt", + # required for now, will be removed in next update + do_scaling=False, + processor=processor, + **inputs, + ) + self.assertTrue( + isinstance(output, list) and isinstance(output[0], PIL.Image.Image), + f"Expected `List[PIL.Image.Image]` output, got {type(output)}", + ) + self.assertEqual( + output[0].height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}" + ) + self.assertEqual( + output[0].width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}" + ) + output_slice = torch.from_numpy(np.array(output[0])[0, -3:, -3:].flatten()) + self.assertTrue( + torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1), + f"{output_slice}", + ) + + def test_output_type_pt(self): + inputs = self.get_dummy_inputs() + processor = self.processor_cls() + output = remote_decode(output_type="pt", processor=processor, **inputs) + self.assertTrue( + isinstance(output, list) and isinstance(output[0], PIL.Image.Image), + f"Expected `List[PIL.Image.Image]` output, got {type(output)}", + ) + self.assertEqual( + output[0].height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}" + ) + self.assertEqual( + output[0].width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}" + ) + output_slice = torch.from_numpy(np.array(output[0])[0, -3:, -3:].flatten()) + self.assertTrue( + torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1), + f"{output_slice}", + ) + + # output is visually the same, slice is flaky? + def test_output_type_pil(self): + inputs = self.get_dummy_inputs() + processor = self.processor_cls() + output = remote_decode(output_type="pil", processor=processor, **inputs) + self.assertTrue( + isinstance(output, list) and isinstance(output[0], PIL.Image.Image), + f"Expected `List[PIL.Image.Image]` output, got {type(output)}", + ) + self.assertEqual( + output[0].height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}" + ) + self.assertEqual( + output[0].width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}" + ) + + def test_output_type_pil_image_format(self): + inputs = self.get_dummy_inputs() + processor = self.processor_cls() + output = remote_decode(output_type="pil", processor=processor, image_format="png", **inputs) + self.assertTrue( + isinstance(output, list) and isinstance(output[0], PIL.Image.Image), + f"Expected `List[PIL.Image.Image]` output, got {type(output)}", + ) + self.assertEqual( + output[0].height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}" + ) + self.assertEqual( + output[0].width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}" + ) + output_slice = torch.from_numpy(np.array(output[0])[0, -3:, -3:].flatten()) + self.assertTrue( + torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1), + f"{output_slice}", + ) + + def test_output_type_pt_partial_postprocess(self): + inputs = self.get_dummy_inputs() + output = remote_decode(output_type="pt", partial_postprocess=True, **inputs) + self.assertTrue( + isinstance(output, list) and isinstance(output[0], PIL.Image.Image), + f"Expected `List[PIL.Image.Image]` output, got {type(output)}", + ) + self.assertEqual( + output[0].height, self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}" + ) + self.assertEqual( + output[0].width, self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}" + ) + output_slice = torch.from_numpy(np.array(output[0])[0, -3:, -3:].flatten()) + self.assertTrue( + torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1), + f"{output_slice}", + ) + + def test_output_type_pt_return_type_pt(self): + inputs = self.get_dummy_inputs() + output = remote_decode(output_type="pt", return_type="pt", **inputs) + self.assertTrue(isinstance(output, torch.Tensor), f"Expected `torch.Tensor` output, got {type(output)}") + self.assertEqual( + output.shape[3], self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.shape[2]}" + ) + self.assertEqual( + output.shape[4], self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.shape[3]}" + ) + output_slice = output[0, 0, 0, -3:, -3:].flatten() + self.assertTrue( + torch_all_close(output_slice, self.return_pt_slice.to(output_slice.dtype), rtol=1e-3, atol=1e-3), + f"{output_slice}", + ) + + def test_output_type_mp4(self): + inputs = self.get_dummy_inputs() + output = remote_decode(output_type="mp4", return_type="mp4", **inputs) + self.assertTrue(isinstance(output, bytes), f"Expected `bytes` output, got {type(output)}") + + +class RemoteAutoencoderKLSDv1Tests( + RemoteAutoencoderKLMixin, + unittest.TestCase, +): + shape = ( + 1, + 4, + 64, + 64, + ) + out_hw = ( + 512, + 512, + ) + endpoint = DECODE_ENDPOINT_SD_V1 + dtype = torch.float16 + scaling_factor = 0.18215 + shift_factor = None + processor_cls = VaeImageProcessor + output_pt_slice = torch.tensor([31, 15, 11, 55, 30, 21, 66, 42, 30], dtype=torch.uint8) + partial_postprocess_return_pt_slice = torch.tensor([100, 130, 99, 133, 106, 112, 97, 100, 121], dtype=torch.uint8) + return_pt_slice = torch.tensor([-0.2177, 0.0217, -0.2258, 0.0412, -0.1687, -0.1232, -0.2416, -0.2130, -0.0543]) + + +class RemoteAutoencoderKLSDXLTests( + RemoteAutoencoderKLMixin, + unittest.TestCase, +): + shape = ( + 1, + 4, + 128, + 128, + ) + out_hw = ( + 1024, + 1024, + ) + endpoint = DECODE_ENDPOINT_SD_XL + dtype = torch.float16 + scaling_factor = 0.13025 + shift_factor = None + processor_cls = VaeImageProcessor + output_pt_slice = torch.tensor([104, 52, 23, 114, 61, 35, 108, 87, 38], dtype=torch.uint8) + partial_postprocess_return_pt_slice = torch.tensor([77, 86, 89, 49, 60, 75, 52, 65, 78], dtype=torch.uint8) + return_pt_slice = torch.tensor([-0.3945, -0.3289, -0.2993, -0.6177, -0.5259, -0.4119, -0.5898, -0.4863, -0.3845]) + + +class RemoteAutoencoderKLFluxTests( + RemoteAutoencoderKLMixin, + unittest.TestCase, +): + shape = ( + 1, + 16, + 128, + 128, + ) + out_hw = ( + 1024, + 1024, + ) + endpoint = DECODE_ENDPOINT_FLUX + dtype = torch.bfloat16 + scaling_factor = 0.3611 + shift_factor = 0.1159 + processor_cls = VaeImageProcessor + output_pt_slice = torch.tensor([110, 72, 91, 62, 35, 52, 69, 55, 69], dtype=torch.uint8) + partial_postprocess_return_pt_slice = torch.tensor( + [202, 203, 203, 197, 195, 193, 189, 188, 178], dtype=torch.uint8 + ) + return_pt_slice = torch.tensor([0.5820, 0.5962, 0.5898, 0.5439, 0.5327, 0.5112, 0.4797, 0.4773, 0.3984]) + + +class RemoteAutoencoderKLFluxPackedTests( + RemoteAutoencoderKLMixin, + unittest.TestCase, +): + shape = ( + 1, + 4096, + 64, + ) + out_hw = ( + 1024, + 1024, + ) + height = 1024 + width = 1024 + endpoint = DECODE_ENDPOINT_FLUX + dtype = torch.bfloat16 + scaling_factor = 0.3611 + shift_factor = 0.1159 + processor_cls = VaeImageProcessor + # slices are different due to randn on different shape. we can pack the latent instead if we want the same + output_pt_slice = torch.tensor([96, 116, 157, 45, 67, 104, 34, 56, 89], dtype=torch.uint8) + partial_postprocess_return_pt_slice = torch.tensor( + [168, 212, 202, 155, 191, 185, 150, 180, 168], dtype=torch.uint8 + ) + return_pt_slice = torch.tensor([0.3198, 0.6631, 0.5864, 0.2131, 0.4944, 0.4482, 0.1776, 0.4153, 0.3176]) + + +class RemoteAutoencoderKLHunyuanVideoTests( + RemoteAutoencoderKLHunyuanVideoMixin, + unittest.TestCase, +): + shape = ( + 1, + 16, + 3, + 40, + 64, + ) + out_hw = ( + 320, + 512, + ) + endpoint = DECODE_ENDPOINT_HUNYUAN_VIDEO + dtype = torch.float16 + scaling_factor = 0.476986 + processor_cls = VideoProcessor + output_pt_slice = torch.tensor([112, 92, 85, 112, 93, 85, 112, 94, 85], dtype=torch.uint8) + partial_postprocess_return_pt_slice = torch.tensor( + [149, 161, 168, 136, 150, 156, 129, 143, 149], dtype=torch.uint8 + ) + return_pt_slice = torch.tensor([0.1656, 0.2661, 0.3157, 0.0693, 0.1755, 0.2252, 0.0127, 0.1221, 0.1708]) + + +class RemoteAutoencoderKLSlowTestMixin: + channels: int = 4 + endpoint: str = None + dtype: torch.dtype = None + scaling_factor: float = None + shift_factor: float = None + width: int = None + height: int = None + + def get_dummy_inputs(self): + inputs = { + "endpoint": self.endpoint, + "scaling_factor": self.scaling_factor, + "shift_factor": self.shift_factor, + "height": self.height, + "width": self.width, + } + return inputs + + def test_multi_res(self): + inputs = self.get_dummy_inputs() + for height in {320, 512, 640, 704, 896, 1024, 1208, 1384, 1536, 1608, 1864, 2048}: + for width in {320, 512, 640, 704, 896, 1024, 1208, 1384, 1536, 1608, 1864, 2048}: + inputs["tensor"] = torch.randn( + (1, self.channels, height // 8, width // 8), + device=torch_device, + dtype=self.dtype, + generator=torch.Generator(torch_device).manual_seed(13), + ) + inputs["height"] = height + inputs["width"] = width + output = remote_decode(output_type="pt", partial_postprocess=True, **inputs) + output.save(f"test_multi_res_{height}_{width}.png") + + +@slow +class RemoteAutoencoderKLSDv1SlowTests( + RemoteAutoencoderKLSlowTestMixin, + unittest.TestCase, +): + endpoint = DECODE_ENDPOINT_SD_V1 + dtype = torch.float16 + scaling_factor = 0.18215 + shift_factor = None + + +@slow +class RemoteAutoencoderKLSDXLSlowTests( + RemoteAutoencoderKLSlowTestMixin, + unittest.TestCase, +): + endpoint = DECODE_ENDPOINT_SD_XL + dtype = torch.float16 + scaling_factor = 0.13025 + shift_factor = None + + +@slow +class RemoteAutoencoderKLFluxSlowTests( + RemoteAutoencoderKLSlowTestMixin, + unittest.TestCase, +): + channels = 16 + endpoint = DECODE_ENDPOINT_FLUX + dtype = torch.bfloat16 + scaling_factor = 0.3611 + shift_factor = 0.1159 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/remote/test_remote_encode.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/remote/test_remote_encode.py new file mode 100644 index 0000000000000000000000000000000000000000..4c0daf08fd8cde87997cb2187862bee898409dbe --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/remote/test_remote_encode.py @@ -0,0 +1,225 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import PIL.Image +import torch + +from diffusers.utils import load_image +from diffusers.utils.constants import ( + DECODE_ENDPOINT_FLUX, + DECODE_ENDPOINT_SD_V1, + DECODE_ENDPOINT_SD_XL, + ENCODE_ENDPOINT_FLUX, + ENCODE_ENDPOINT_SD_V1, + ENCODE_ENDPOINT_SD_XL, +) +from diffusers.utils.remote_utils import ( + remote_decode, + remote_encode, +) + +from ..testing_utils import ( + enable_full_determinism, + slow, +) + + +enable_full_determinism() + +IMAGE = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg?download=true" + + +class RemoteAutoencoderKLEncodeMixin: + channels: int = None + endpoint: str = None + decode_endpoint: str = None + dtype: torch.dtype = None + scaling_factor: float = None + shift_factor: float = None + image: PIL.Image.Image = None + + def get_dummy_inputs(self): + if self.image is None: + self.image = load_image(IMAGE) + inputs = { + "endpoint": self.endpoint, + "image": self.image, + "scaling_factor": self.scaling_factor, + "shift_factor": self.shift_factor, + } + return inputs + + def test_image_input(self): + inputs = self.get_dummy_inputs() + height, width = inputs["image"].height, inputs["image"].width + output = remote_encode(**inputs) + self.assertEqual(list(output.shape), [1, self.channels, height // 8, width // 8]) + decoded = remote_decode( + tensor=output, + endpoint=self.decode_endpoint, + scaling_factor=self.scaling_factor, + shift_factor=self.shift_factor, + image_format="png", + ) + self.assertEqual(decoded.height, height) + self.assertEqual(decoded.width, width) + # image_slice = torch.from_numpy(np.array(inputs["image"])[0, -3:, -3:].flatten()) + # decoded_slice = torch.from_numpy(np.array(decoded)[0, -3:, -3:].flatten()) + # TODO: how to test this? encode->decode is lossy. expected slice of encoded latent? + + +class RemoteAutoencoderKLSDv1Tests( + RemoteAutoencoderKLEncodeMixin, + unittest.TestCase, +): + channels = 4 + endpoint = ENCODE_ENDPOINT_SD_V1 + decode_endpoint = DECODE_ENDPOINT_SD_V1 + dtype = torch.float16 + scaling_factor = 0.18215 + shift_factor = None + + +class RemoteAutoencoderKLSDXLTests( + RemoteAutoencoderKLEncodeMixin, + unittest.TestCase, +): + channels = 4 + endpoint = ENCODE_ENDPOINT_SD_XL + decode_endpoint = DECODE_ENDPOINT_SD_XL + dtype = torch.float16 + scaling_factor = 0.13025 + shift_factor = None + + +class RemoteAutoencoderKLFluxTests( + RemoteAutoencoderKLEncodeMixin, + unittest.TestCase, +): + channels = 16 + endpoint = ENCODE_ENDPOINT_FLUX + decode_endpoint = DECODE_ENDPOINT_FLUX + dtype = torch.bfloat16 + scaling_factor = 0.3611 + shift_factor = 0.1159 + + +class RemoteAutoencoderKLEncodeSlowTestMixin: + channels: int = 4 + endpoint: str = None + decode_endpoint: str = None + dtype: torch.dtype = None + scaling_factor: float = None + shift_factor: float = None + image: PIL.Image.Image = None + + def get_dummy_inputs(self): + if self.image is None: + self.image = load_image(IMAGE) + inputs = { + "endpoint": self.endpoint, + "image": self.image, + "scaling_factor": self.scaling_factor, + "shift_factor": self.shift_factor, + } + return inputs + + def test_multi_res(self): + inputs = self.get_dummy_inputs() + for height in { + 320, + 512, + 640, + 704, + 896, + 1024, + 1208, + 1384, + 1536, + 1608, + 1864, + 2048, + }: + for width in { + 320, + 512, + 640, + 704, + 896, + 1024, + 1208, + 1384, + 1536, + 1608, + 1864, + 2048, + }: + inputs["image"] = inputs["image"].resize( + ( + width, + height, + ) + ) + output = remote_encode(**inputs) + self.assertEqual(list(output.shape), [1, self.channels, height // 8, width // 8]) + decoded = remote_decode( + tensor=output, + endpoint=self.decode_endpoint, + scaling_factor=self.scaling_factor, + shift_factor=self.shift_factor, + image_format="png", + ) + self.assertEqual(decoded.height, height) + self.assertEqual(decoded.width, width) + decoded.save(f"test_multi_res_{height}_{width}.png") + + +@slow +class RemoteAutoencoderKLSDv1SlowTests( + RemoteAutoencoderKLEncodeSlowTestMixin, + unittest.TestCase, +): + endpoint = ENCODE_ENDPOINT_SD_V1 + decode_endpoint = DECODE_ENDPOINT_SD_V1 + dtype = torch.float16 + scaling_factor = 0.18215 + shift_factor = None + + +@slow +class RemoteAutoencoderKLSDXLSlowTests( + RemoteAutoencoderKLEncodeSlowTestMixin, + unittest.TestCase, +): + endpoint = ENCODE_ENDPOINT_SD_XL + decode_endpoint = DECODE_ENDPOINT_SD_XL + dtype = torch.float16 + scaling_factor = 0.13025 + shift_factor = None + + +@slow +class RemoteAutoencoderKLFluxSlowTests( + RemoteAutoencoderKLEncodeSlowTestMixin, + unittest.TestCase, +): + channels = 16 + endpoint = ENCODE_ENDPOINT_FLUX + decode_endpoint = DECODE_ENDPOINT_FLUX + dtype = torch.bfloat16 + scaling_factor = 0.3611 + shift_factor = 0.1159 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_block_refinement.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_block_refinement.py new file mode 100644 index 0000000000000000000000000000000000000000..2e5e404e5f9a26e089a39c133c3921398f5792bc --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_block_refinement.py @@ -0,0 +1,470 @@ +import tempfile +import unittest + +import torch + +from diffusers import BlockRefinementScheduler + + +class BlockRefinementSchedulerTest(unittest.TestCase): + def get_scheduler(self, **kwargs): + config = { + "block_length": 32, + "num_inference_steps": 8, + "threshold": 0.95, + "editing_threshold": None, + "minimal_topk": 1, + } + config.update(kwargs) + return BlockRefinementScheduler(**config) + + def _make_logits_from_probs(self, target_probs: torch.Tensor, vocab_size: int = 100) -> torch.Tensor: + """Create logits where softmax of the target token has approximately the given probability.""" + batch_size, block_length = target_probs.shape + logits = torch.zeros(batch_size, block_length, vocab_size) + # Set token 0 as the "predicted" token with a logit proportional to desired probability + for b in range(batch_size): + for t in range(block_length): + p = target_probs[b, t].item() + if p > 0: + logits[b, t, t % (vocab_size - 1)] = 10.0 * p + return logits + + def test_set_timesteps(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(8) + self.assertEqual(scheduler.num_inference_steps, 8) + self.assertEqual(len(scheduler.timesteps), 8) + self.assertEqual(scheduler.timesteps[0].item(), 7) + self.assertEqual(scheduler.timesteps[-1].item(), 0) + + def test_set_timesteps_invalid(self): + scheduler = self.get_scheduler() + with self.assertRaises(ValueError): + scheduler.set_timesteps(0) + + def test_get_num_transfer_tokens_even(self): + scheduler = self.get_scheduler() + schedule = scheduler.get_num_transfer_tokens(block_length=32, num_inference_steps=8) + self.assertEqual(schedule.sum().item(), 32) + self.assertEqual(len(schedule), 8) + self.assertTrue((schedule == 4).all().item()) + + def test_get_num_transfer_tokens_remainder(self): + scheduler = self.get_scheduler() + schedule = scheduler.get_num_transfer_tokens(block_length=10, num_inference_steps=3) + self.assertEqual(schedule.sum().item(), 10) + self.assertEqual(len(schedule), 3) + self.assertEqual(schedule[0].item(), 4) + self.assertEqual(schedule[1].item(), 3) + self.assertEqual(schedule[2].item(), 3) + + def test_transfer_schedule_created_on_set_timesteps(self): + scheduler = self.get_scheduler(block_length=16) + scheduler.set_timesteps(4) + self.assertIsNotNone(scheduler._transfer_schedule) + self.assertEqual(scheduler._transfer_schedule.sum().item(), 16) + + def test_save_load_config_round_trip(self): + scheduler = self.get_scheduler(block_length=64, threshold=0.8, editing_threshold=0.5, minimal_topk=2) + with tempfile.TemporaryDirectory() as tmpdir: + scheduler.save_config(tmpdir) + loaded = BlockRefinementScheduler.from_pretrained(tmpdir) + + self.assertEqual(loaded.config.block_length, 64) + self.assertEqual(loaded.config.threshold, 0.8) + self.assertEqual(loaded.config.editing_threshold, 0.5) + self.assertEqual(loaded.config.minimal_topk, 2) + + def test_from_config(self): + scheduler = self.get_scheduler(block_length=16, threshold=0.7) + new_scheduler = BlockRefinementScheduler.from_config(scheduler.config) + self.assertEqual(new_scheduler.config.block_length, 16) + self.assertEqual(new_scheduler.config.threshold, 0.7) + + def test_step_commits_tokens(self): + """Verify that step() commits mask tokens based on confidence.""" + scheduler = self.get_scheduler(block_length=8) + scheduler.set_timesteps(2) + + batch_size, block_length, vocab_size = 1, 8, 32 + mask_id = 31 + + sample = torch.full((batch_size, block_length), mask_id, dtype=torch.long) + # Create logits where confidence decreases with position + logits = torch.zeros(batch_size, block_length, vocab_size) + for i in range(block_length): + logits[0, i, i] = 10.0 - i # decreasing confidence + + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + temperature=0.0, + threshold=0.95, + return_dict=True, + ) + + # With 8 tokens and 2 steps, first step should commit 4 tokens + committed = out.transfer_index[0].sum().item() + self.assertEqual(committed, 4) + + def test_step_no_editing_by_default(self): + """Without editing_threshold, no non-mask tokens should be changed.""" + scheduler = self.get_scheduler(block_length=4) + scheduler.set_timesteps(2) + + vocab_size = 32 + sample = torch.tensor([[10, 20, 31, 31]], dtype=torch.long) + logits = torch.zeros(1, 4, vocab_size) + logits[0, :, 15] = 10.0 # predict token 15 for all positions + + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=31, + temperature=0.0, + editing_threshold=None, + return_dict=True, + ) + + self.assertFalse(out.editing_transfer_index.any().item()) + self.assertFalse(out.transfer_index[0, 0].item()) + self.assertFalse(out.transfer_index[0, 1].item()) + + def test_step_editing_replaces_tokens(self): + """With editing_threshold, non-mask tokens with high confidence and different prediction get replaced.""" + scheduler = self.get_scheduler(block_length=4) + scheduler.set_timesteps(2) + + vocab_size = 32 + sample = torch.tensor([[10, 20, 31, 31]], dtype=torch.long) + logits = torch.zeros(1, 4, vocab_size) + # Token 0: predict 50 (different from 10) with very high logit + logits[0, 0, 15] = 20.0 + # Token 1: predict 20 (same as current) + logits[0, 1, 20] = 20.0 + # Mask tokens + logits[0, 2, 5] = 5.0 + logits[0, 3, 6] = 5.0 + + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=31, + temperature=0.0, + editing_threshold=0.5, + return_dict=True, + ) + + # Token 0 should be edited (different prediction, high confidence) + self.assertTrue(out.editing_transfer_index[0, 0].item()) + # Token 1 should NOT be edited (same prediction) + self.assertFalse(out.editing_transfer_index[0, 1].item()) + + def test_step_prompt_mask_prevents_editing(self): + """Prompt positions should never be edited even with editing enabled.""" + scheduler = self.get_scheduler(block_length=4) + scheduler.set_timesteps(2) + + vocab_size = 32 + sample = torch.tensor([[10, 20, 31, 31]], dtype=torch.long) + logits = torch.zeros(1, 4, vocab_size) + logits[0, :, 15] = 20.0 + prompt_mask = torch.tensor([True, True, False, False]) + + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=31, + temperature=0.0, + editing_threshold=0.5, + prompt_mask=prompt_mask, + return_dict=True, + ) + + self.assertFalse(out.editing_transfer_index[0, 0].item()) + self.assertFalse(out.editing_transfer_index[0, 1].item()) + + def test_step_return_tuple(self): + """Verify tuple output when return_dict=False.""" + scheduler = self.get_scheduler(block_length=4) + scheduler.set_timesteps(2) + + vocab_size = 32 + sample = torch.full((1, 4), 31, dtype=torch.long) + logits = torch.randn(1, 4, vocab_size) + + result = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=31, + temperature=0.0, + return_dict=False, + ) + + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 5) + + def test_step_batched(self): + """Verify step works with batch_size > 1.""" + scheduler = self.get_scheduler(block_length=4) + scheduler.set_timesteps(2) + + batch_size, vocab_size = 3, 32 + mask_id = 31 + sample = torch.full((batch_size, 4), mask_id, dtype=torch.long) + logits = torch.randn(batch_size, 4, vocab_size) + + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + temperature=0.0, + return_dict=True, + ) + + self.assertEqual(out.prev_sample.shape, (batch_size, 4)) + self.assertEqual(out.transfer_index.shape, (batch_size, 4)) + + def test_check_block_should_continue_finished(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(8) + finished = torch.tensor([True, True]) + result = scheduler.check_block_should_continue( + step_idx=0, + masks_remaining=True, + editing_enabled=False, + editing_transfer_index=torch.zeros(2, 32, dtype=torch.bool), + post_steps=0, + max_post_steps=16, + finished=finished, + ) + self.assertFalse(result) + + def test_check_block_should_continue_no_masks_no_edits(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(8) + finished = torch.tensor([False]) + result = scheduler.check_block_should_continue( + step_idx=5, + masks_remaining=False, + editing_enabled=True, + editing_transfer_index=torch.zeros(1, 32, dtype=torch.bool), + post_steps=1, + max_post_steps=16, + finished=finished, + ) + self.assertFalse(result) + + def test_check_block_should_continue_steps_exhausted(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(8) + finished = torch.tensor([False]) + result = scheduler.check_block_should_continue( + step_idx=8, + masks_remaining=True, + editing_enabled=False, + editing_transfer_index=torch.zeros(1, 32, dtype=torch.bool), + post_steps=0, + max_post_steps=16, + finished=finished, + ) + self.assertFalse(result) + + def test_check_eos_finished_marks_batch(self): + """When EOS is committed and all tokens before it are unmasked, mark batch as finished.""" + mask_id, eos_id, prompt_length = 99, 2, 2 + # cur_x: [prompt, prompt, token, eos, mask, mask] + cur_x = torch.tensor([[10, 11, 5, eos_id, mask_id, mask_id]], dtype=torch.long) + sampled_tokens = torch.tensor([[0, 0, 0, eos_id]], dtype=torch.long) + final_transfer = torch.tensor([[False, False, False, True]]) + finished = torch.tensor([False]) + + finished = BlockRefinementScheduler.check_eos_finished( + cur_x=cur_x, + sampled_tokens=sampled_tokens, + final_transfer=final_transfer, + finished=finished, + eos_token_id=eos_id, + mask_token_id=mask_id, + prompt_length=prompt_length, + ) + self.assertTrue(finished[0].item()) + + def test_check_eos_finished_ignores_when_masks_before_eos(self): + """If there are still mask tokens between prompt and EOS, don't mark as finished.""" + mask_id, eos_id, prompt_length = 99, 2, 2 + # cur_x: [prompt, prompt, mask, eos] — mask before EOS + cur_x = torch.tensor([[10, 11, mask_id, eos_id]], dtype=torch.long) + sampled_tokens = torch.tensor([[0, 0]], dtype=torch.long) + final_transfer = torch.tensor([[False, True]]) + finished = torch.tensor([False]) + + finished = BlockRefinementScheduler.check_eos_finished( + cur_x=cur_x, + sampled_tokens=sampled_tokens, + final_transfer=final_transfer, + finished=finished, + eos_token_id=eos_id, + mask_token_id=mask_id, + prompt_length=prompt_length, + ) + self.assertFalse(finished[0].item()) + + def test_check_eos_finished_already_finished(self): + """Already-finished batches should stay finished.""" + mask_id, eos_id = 99, 2 + cur_x = torch.tensor([[10, 11, 5, 6]], dtype=torch.long) + sampled_tokens = torch.tensor([[0, 0]], dtype=torch.long) + final_transfer = torch.tensor([[False, False]]) + finished = torch.tensor([True]) + + finished = BlockRefinementScheduler.check_eos_finished( + cur_x=cur_x, + sampled_tokens=sampled_tokens, + final_transfer=final_transfer, + finished=finished, + eos_token_id=eos_id, + mask_token_id=mask_id, + prompt_length=2, + ) + self.assertTrue(finished[0].item()) + + def test_add_noise(self): + scheduler = self.get_scheduler(block_length=4) + input_ids = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=torch.long) + attention_mask = torch.ones_like(input_ids) + mask_token_id = 99 + + gen = torch.Generator().manual_seed(42) + noisy, noisy_rev, masked, masked_rev = scheduler.add_noise( + input_ids, + attention_mask, + prompt_length=2, + block_length=4, + mask_token_id=mask_token_id, + generator=gen, + ) + + # Prompt positions should never be masked + self.assertFalse(masked[0, 0].item()) + self.assertFalse(masked[0, 1].item()) + self.assertFalse(masked_rev[0, 0].item()) + self.assertFalse(masked_rev[0, 1].item()) + + # Noisy should have mask_token_id where masked is True + self.assertTrue((noisy[masked] == mask_token_id).all().item()) + self.assertTrue((noisy_rev[masked_rev] == mask_token_id).all().item()) + + # masked and masked_rev should be complementary within valid non-prompt positions + non_prompt = torch.zeros_like(masked) + non_prompt[0, 2:] = True + combined = masked | masked_rev + self.assertTrue((combined[0, 2:] == non_prompt[0, 2:]).all().item()) + + +class TestTopPFiltering(unittest.TestCase): + def test_top_p_filtering(self): + logits = torch.tensor([[1.0, 2.0, 3.0, 4.0]]) + filtered = BlockRefinementScheduler._top_p_filtering(logits, top_p=0.5) + self.assertTrue((filtered > torch.finfo(filtered.dtype).min).any()) + self.assertTrue((filtered == torch.finfo(filtered.dtype).min).any()) + + def test_top_p_filtering_none(self): + logits = torch.tensor([[1.0, 2.0, 3.0]]) + result = BlockRefinementScheduler._top_p_filtering(logits, top_p=None) + self.assertTrue(torch.equal(result, logits)) + + def test_top_p_filtering_one(self): + logits = torch.tensor([[1.0, 2.0, 3.0]]) + result = BlockRefinementScheduler._top_p_filtering(logits, top_p=1.0) + self.assertTrue(torch.equal(result, logits)) + + +class TestTopKFiltering(unittest.TestCase): + def test_top_k_filtering(self): + logits = torch.tensor([[1.0, 4.0, 2.0, 3.0]]) + filtered = BlockRefinementScheduler._top_k_filtering(logits, top_k=2) + self.assertAlmostEqual(filtered[0, 1].item(), 4.0) + self.assertAlmostEqual(filtered[0, 3].item(), 3.0) + self.assertEqual(filtered[0, 0].item(), torch.finfo(filtered.dtype).min) + self.assertEqual(filtered[0, 2].item(), torch.finfo(filtered.dtype).min) + + def test_top_k_filtering_none(self): + logits = torch.tensor([[1.0, 2.0, 3.0]]) + result = BlockRefinementScheduler._top_k_filtering(logits, top_k=None) + self.assertTrue(torch.equal(result, logits)) + + def test_top_k_filtering_zero(self): + logits = torch.tensor([[1.0, 2.0, 3.0]]) + result = BlockRefinementScheduler._top_k_filtering(logits, top_k=0) + self.assertTrue(torch.equal(result, logits)) + + def test_top_k_filtering_large_k(self): + logits = torch.tensor([[1.0, 2.0, 3.0]]) + result = BlockRefinementScheduler._top_k_filtering(logits, top_k=100) + self.assertTrue(torch.equal(result, logits)) + + +class TestSampleFromLogits(unittest.TestCase): + def test_greedy_sampling(self): + logits = torch.tensor([[1.0, 5.0, 2.0]]) + tokens, probs = BlockRefinementScheduler._sample_from_logits( + logits, + temperature=0.0, + top_k=None, + top_p=None, + generator=None, + use_multinomial=False, + ) + self.assertEqual(tokens.item(), 1) + self.assertEqual(tokens.shape, (1,)) + self.assertEqual(probs.shape, (1,)) + + def test_multinomial_sampling(self): + logits = torch.tensor([[0.0, 100.0, -100.0]]) + gen = torch.Generator().manual_seed(42) + tokens, probs = BlockRefinementScheduler._sample_from_logits( + logits, + temperature=1.0, + top_k=None, + top_p=None, + generator=gen, + use_multinomial=True, + ) + self.assertEqual(tokens.item(), 1) + + def test_temperature_scaling(self): + logits = torch.tensor([[1.0, 2.0, 3.0]]) + tokens, _ = BlockRefinementScheduler._sample_from_logits( + logits, + temperature=0.01, + top_k=None, + top_p=None, + generator=None, + use_multinomial=False, + ) + self.assertEqual(tokens.item(), 2) + + def test_negative_temperature_raises(self): + logits = torch.tensor([[1.0, 2.0]]) + with self.assertRaises(ValueError): + BlockRefinementScheduler._sample_from_logits( + logits, + temperature=-1.0, + top_k=None, + top_p=None, + generator=None, + use_multinomial=False, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_consistency_model.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_consistency_model.py new file mode 100644 index 0000000000000000000000000000000000000000..4f773d7db05f752d2b1e216071af5a1ca808d658 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_consistency_model.py @@ -0,0 +1,189 @@ +import torch + +from diffusers import CMStochasticIterativeScheduler + +from .test_schedulers import SchedulerCommonTest + + +class CMStochasticIterativeSchedulerTest(SchedulerCommonTest): + scheduler_classes = (CMStochasticIterativeScheduler,) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 201, + "sigma_min": 0.002, + "sigma_max": 80.0, + } + + config.update(**kwargs) + return config + + # Override test_step_shape to add CMStochasticIterativeScheduler-specific logic regarding timesteps + # Problem is that we don't know two timesteps that will always be in the timestep schedule from only the scheduler + # config; scaled sigma_max is always in the timestep schedule, but sigma_min is in the sigma schedule while scaled + # sigma_min is not in the timestep schedule + def test_step_shape(self): + num_inference_steps = 10 + + scheduler_config = self.get_scheduler_config() + scheduler = self.scheduler_classes[0](**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + + timestep_0 = scheduler.timesteps[0] + timestep_1 = scheduler.timesteps[1] + + sample = self.dummy_sample + residual = 0.1 * sample + + output_0 = scheduler.step(residual, timestep_0, sample).prev_sample + output_1 = scheduler.step(residual, timestep_1, sample).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_clip_denoised(self): + for clip_denoised in [True, False]: + self.check_over_configs(clip_denoised=clip_denoised) + + def test_full_loop_no_noise_onestep(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 1 + scheduler.set_timesteps(num_inference_steps) + timesteps = scheduler.timesteps + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + for i, t in enumerate(timesteps): + # 1. scale model input + scaled_sample = scheduler.scale_model_input(sample, t) + + # 2. predict noise residual + residual = model(scaled_sample, t) + + # 3. predict previous sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 192.7614) < 1e-2 + assert abs(result_mean.item() - 0.2510) < 1e-3 + + def test_full_loop_no_noise_multistep(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [106, 0] + scheduler.set_timesteps(timesteps=timesteps) + timesteps = scheduler.timesteps + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + for t in timesteps: + # 1. scale model input + scaled_sample = scheduler.scale_model_input(sample, t) + + # 2. predict noise residual + residual = model(scaled_sample, t) + + # 3. predict previous sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 347.6357) < 1e-2 + assert abs(result_mean.item() - 0.4527) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + t_start = 8 + + scheduler.set_timesteps(num_inference_steps) + timesteps = scheduler.timesteps + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for t in timesteps: + # 1. scale model input + scaled_sample = scheduler.scale_model_input(sample, t) + + # 2. predict noise residual + residual = model(scaled_sample, t) + + # 3. predict previous sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 763.9186) < 1e-2, f" expected result sum 763.9186, but get {result_sum}" + assert abs(result_mean.item() - 0.9947) < 1e-3, f" expected result mean 0.9947, but get {result_mean}" + + def test_custom_timesteps_increasing_order(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [39, 30, 12, 15, 0] + + with self.assertRaises(ValueError, msg="`timesteps` must be in descending order."): + scheduler.set_timesteps(timesteps=timesteps) + + def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [39, 30, 12, 1, 0] + num_inference_steps = len(timesteps) + + with self.assertRaises(ValueError, msg="Can only pass one of `num_inference_steps` or `timesteps`."): + scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps) + + def test_custom_timesteps_too_large(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [scheduler.config.num_train_timesteps] + + with self.assertRaises( + ValueError, + msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}", + ): + scheduler.set_timesteps(timesteps=timesteps) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..13b353a44b0885ed52eda0ce97d3ca7bd4b18ad5 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim.py @@ -0,0 +1,176 @@ +import torch + +from diffusers import DDIMScheduler + +from .test_schedulers import SchedulerCommonTest + + +class DDIMSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DDIMScheduler,) + forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50)) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "clip_sample": True, + } + + config.update(**kwargs) + return config + + def full_loop(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps, eta = 10, 0.0 + + model = self.dummy_model() + sample = self.dummy_sample_deter + + scheduler.set_timesteps(num_inference_steps) + + for t in scheduler.timesteps: + residual = model(sample, t) + sample = scheduler.step(residual, t, sample, eta).prev_sample + + return sample + + def test_timesteps(self): + for timesteps in [100, 500, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_steps_offset(self): + for steps_offset in [0, 1]: + self.check_over_configs(steps_offset=steps_offset) + + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(steps_offset=1) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(5) + assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1])) + + def test_betas(self): + for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "squaredcos_cap_v2"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_clip_sample(self): + for clip_sample in [True, False]: + self.check_over_configs(clip_sample=clip_sample) + + def test_timestep_spacing(self): + for timestep_spacing in ["trailing", "leading"]: + self.check_over_configs(timestep_spacing=timestep_spacing) + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + ) + + def test_time_indices(self): + for t in [1, 10, 49]: + self.check_over_forward(time_step=t) + + def test_inference_steps(self): + for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]): + self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) + + def test_eta(self): + for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]): + self.check_over_forward(time_step=t, eta=eta) + + def test_variance(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5 + + def test_full_loop_no_noise(self): + sample = self.full_loop() + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 172.0067) < 1e-2 + assert abs(result_mean.item() - 0.223967) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 52.5302) < 1e-2 + assert abs(result_mean.item() - 0.0684) < 1e-3 + + def test_full_loop_with_set_alpha_to_one(self): + # We specify different beta, so that the first alpha is 0.99 + sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01) + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 149.8295) < 1e-2 + assert abs(result_mean.item() - 0.1951) < 1e-3 + + def test_full_loop_with_no_set_alpha_to_one(self): + # We specify different beta, so that the first alpha is 0.99 + sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01) + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 149.0784) < 1e-2 + assert abs(result_mean.item() - 0.1941) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps, eta = 10, 0.0 + t_start = 8 + + model = self.dummy_model() + sample = self.dummy_sample_deter + + scheduler.set_timesteps(num_inference_steps) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for t in timesteps: + residual = model(sample, t) + sample = scheduler.step(residual, t, sample, eta).prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 354.5418) < 1e-2, f" expected result sum 218.4379, but get {result_sum}" + assert abs(result_mean.item() - 0.4616) < 1e-3, f" expected result mean 0.2844, but get {result_mean}" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim_inverse.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim_inverse.py new file mode 100644 index 0000000000000000000000000000000000000000..81d53f1b477850f556ea8cf5d669698ebca295b0 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim_inverse.py @@ -0,0 +1,138 @@ +import unittest + +import torch + +from diffusers import DDIMInverseScheduler + +from .test_schedulers import SchedulerCommonTest + + +class DDIMInverseSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DDIMInverseScheduler,) + forward_default_kwargs = (("num_inference_steps", 50),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "clip_sample": True, + } + + config.update(**kwargs) + return config + + def full_loop(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + + model = self.dummy_model() + sample = self.dummy_sample_deter + + scheduler.set_timesteps(num_inference_steps) + + for t in scheduler.timesteps: + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def test_timesteps(self): + for timesteps in [100, 500, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_steps_offset(self): + for steps_offset in [0, 1]: + self.check_over_configs(steps_offset=steps_offset) + + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(steps_offset=1) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(5) + assert torch.equal(scheduler.timesteps, torch.LongTensor([1, 201, 401, 601, 801])) + + def test_betas(self): + for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "squaredcos_cap_v2"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_clip_sample(self): + for clip_sample in [True, False]: + self.check_over_configs(clip_sample=clip_sample) + + def test_timestep_spacing(self): + for timestep_spacing in ["trailing", "leading"]: + self.check_over_configs(timestep_spacing=timestep_spacing) + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + ) + + def test_time_indices(self): + for t in [1, 10, 49]: + self.check_over_forward(time_step=t) + + def test_inference_steps(self): + for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]): + self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) + + @unittest.skip("Test not supported.") + def test_add_noise_device(self): + pass + + def test_full_loop_no_noise(self): + sample = self.full_loop() + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 671.6816) < 1e-2 + assert abs(result_mean.item() - 0.8746) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 1394.2185) < 1e-2 + assert abs(result_mean.item() - 1.8154) < 1e-3 + + def test_full_loop_with_set_alpha_to_one(self): + # We specify different beta, so that the first alpha is 0.99 + sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01) + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 539.9622) < 1e-2 + assert abs(result_mean.item() - 0.7031) < 1e-3 + + def test_full_loop_with_no_set_alpha_to_one(self): + # We specify different beta, so that the first alpha is 0.99 + sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01) + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 542.6722) < 1e-2 + assert abs(result_mean.item() - 0.7066) < 1e-3 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim_parallel.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..3ce8034cfb952bc1d1e39c4e37621105e0d453a2 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddim_parallel.py @@ -0,0 +1,216 @@ +# Copyright 2025 ParaDiGMS authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from diffusers import DDIMParallelScheduler + +from .test_schedulers import SchedulerCommonTest + + +class DDIMParallelSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DDIMParallelScheduler,) + forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50)) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "clip_sample": True, + } + + config.update(**kwargs) + return config + + def full_loop(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps, eta = 10, 0.0 + + model = self.dummy_model() + sample = self.dummy_sample_deter + + scheduler.set_timesteps(num_inference_steps) + + for t in scheduler.timesteps: + residual = model(sample, t) + sample = scheduler.step(residual, t, sample, eta).prev_sample + + return sample + + def test_timesteps(self): + for timesteps in [100, 500, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_steps_offset(self): + for steps_offset in [0, 1]: + self.check_over_configs(steps_offset=steps_offset) + + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(steps_offset=1) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(5) + assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1])) + + def test_betas(self): + for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "squaredcos_cap_v2"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_clip_sample(self): + for clip_sample in [True, False]: + self.check_over_configs(clip_sample=clip_sample) + + def test_timestep_spacing(self): + for timestep_spacing in ["trailing", "leading"]: + self.check_over_configs(timestep_spacing=timestep_spacing) + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + ) + + def test_time_indices(self): + for t in [1, 10, 49]: + self.check_over_forward(time_step=t) + + def test_inference_steps(self): + for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]): + self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) + + def test_eta(self): + for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]): + self.check_over_forward(time_step=t, eta=eta) + + def test_variance(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5 + + def test_batch_step_no_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps, eta = 10, 0.0 + scheduler.set_timesteps(num_inference_steps) + + model = self.dummy_model() + sample1 = self.dummy_sample_deter + sample2 = self.dummy_sample_deter + 0.1 + sample3 = self.dummy_sample_deter - 0.1 + + per_sample_batch = sample1.shape[0] + samples = torch.stack([sample1, sample2, sample3], dim=0) + timesteps = torch.arange(num_inference_steps)[0:3, None].repeat(1, per_sample_batch) + + residual = model(samples.flatten(0, 1), timesteps.flatten(0, 1)) + pred_prev_sample = scheduler.batch_step_no_noise(residual, timesteps.flatten(0, 1), samples.flatten(0, 1), eta) + + result_sum = torch.sum(torch.abs(pred_prev_sample)) + result_mean = torch.mean(torch.abs(pred_prev_sample)) + + assert abs(result_sum.item() - 1147.7904) < 1e-2 + assert abs(result_mean.item() - 0.4982) < 1e-3 + + def test_full_loop_no_noise(self): + sample = self.full_loop() + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 172.0067) < 1e-2 + assert abs(result_mean.item() - 0.223967) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 52.5302) < 1e-2 + assert abs(result_mean.item() - 0.0684) < 1e-3 + + def test_full_loop_with_set_alpha_to_one(self): + # We specify different beta, so that the first alpha is 0.99 + sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01) + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 149.8295) < 1e-2 + assert abs(result_mean.item() - 0.1951) < 1e-3 + + def test_full_loop_with_no_set_alpha_to_one(self): + # We specify different beta, so that the first alpha is 0.99 + sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01) + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 149.0784) < 1e-2 + assert abs(result_mean.item() - 0.1941) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps, eta = 10, 0.0 + t_start = 8 + + model = self.dummy_model() + sample = self.dummy_sample_deter + + scheduler.set_timesteps(num_inference_steps) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for t in timesteps: + residual = model(sample, t) + sample = scheduler.step(residual, t, sample, eta).prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 354.5418) < 1e-2, f" expected result sum 354.5418, but get {result_sum}" + assert abs(result_mean.item() - 0.4616) < 1e-3, f" expected result mean 0.4616, but get {result_mean}" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddpm.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddpm.py new file mode 100644 index 0000000000000000000000000000000000000000..056b5d83350e4baeadf24693d2597fb185d44f46 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddpm.py @@ -0,0 +1,222 @@ +import torch + +from diffusers import DDPMScheduler + +from .test_schedulers import SchedulerCommonTest + + +class DDPMSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DDPMScheduler,) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "variance_type": "fixed_small", + "clip_sample": True, + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [1, 5, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "squaredcos_cap_v2"]: + self.check_over_configs(beta_schedule=schedule) + + def test_variance_type(self): + for variance in ["fixed_small", "fixed_large", "other"]: + self.check_over_configs(variance_type=variance) + + def test_clip_sample(self): + for clip_sample in [True, False]: + self.check_over_configs(clip_sample=clip_sample) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "sample", "v_prediction"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "sample", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_time_indices(self): + for t in [0, 500, 999]: + self.check_over_forward(time_step=t) + + def test_variance(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5 + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def test_full_loop_no_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_trained_timesteps = len(scheduler) + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(0) + + for t in reversed(range(num_trained_timesteps)): + # 1. predict noise residual + residual = model(sample, t) + + # 2. predict previous mean of sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + # if t > 0: + # noise = self.dummy_sample_deter + # variance = scheduler.get_variance(t) ** (0.5) * noise + # + # sample = pred_prev_sample + variance + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 258.9606) < 1e-2 + assert abs(result_mean.item() - 0.3372) < 1e-3 + + def test_full_loop_with_v_prediction(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") + scheduler = scheduler_class(**scheduler_config) + + num_trained_timesteps = len(scheduler) + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(0) + + for t in reversed(range(num_trained_timesteps)): + # 1. predict noise residual + residual = model(sample, t) + + # 2. predict previous mean of sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + # if t > 0: + # noise = self.dummy_sample_deter + # variance = scheduler.get_variance(t) ** (0.5) * noise + # + # sample = pred_prev_sample + variance + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 202.0296) < 1e-2 + assert abs(result_mean.item() - 0.2631) < 1e-3 + + def test_custom_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 1, 0] + + scheduler.set_timesteps(timesteps=timesteps) + + scheduler_timesteps = scheduler.timesteps + + for i, timestep in enumerate(scheduler_timesteps): + if i == len(timesteps) - 1: + expected_prev_t = -1 + else: + expected_prev_t = timesteps[i + 1] + + prev_t = scheduler.previous_timestep(timestep) + prev_t = prev_t.item() + + self.assertEqual(prev_t, expected_prev_t) + + def test_custom_timesteps_increasing_order(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 51, 0] + + with self.assertRaises(ValueError, msg="`custom_timesteps` must be in descending order."): + scheduler.set_timesteps(timesteps=timesteps) + + def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 1, 0] + num_inference_steps = len(timesteps) + + with self.assertRaises(ValueError, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."): + scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps) + + def test_custom_timesteps_too_large(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [scheduler.config.num_train_timesteps] + + with self.assertRaises( + ValueError, + msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}", + ): + scheduler.set_timesteps(timesteps=timesteps) + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_trained_timesteps = len(scheduler) + t_start = num_trained_timesteps - 2 + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(0) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for t in timesteps: + # 1. predict noise residual + residual = model(sample, t) + + # 2. predict previous mean of sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 387.9466) < 1e-2, f" expected result sum 387.9466, but get {result_sum}" + assert abs(result_mean.item() - 0.5051) < 1e-3, f" expected result mean 0.5051, but get {result_mean}" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddpm_parallel.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddpm_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..377067071c2568d247e113bea2e3e1347a4d31ca --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ddpm_parallel.py @@ -0,0 +1,251 @@ +# Copyright 2025 ParaDiGMS authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from diffusers import DDPMParallelScheduler + +from .test_schedulers import SchedulerCommonTest + + +class DDPMParallelSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DDPMParallelScheduler,) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "variance_type": "fixed_small", + "clip_sample": True, + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [1, 5, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "squaredcos_cap_v2"]: + self.check_over_configs(beta_schedule=schedule) + + def test_variance_type(self): + for variance in ["fixed_small", "fixed_large", "other"]: + self.check_over_configs(variance_type=variance) + + def test_clip_sample(self): + for clip_sample in [True, False]: + self.check_over_configs(clip_sample=clip_sample) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "sample", "v_prediction"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "sample", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_time_indices(self): + for t in [0, 500, 999]: + self.check_over_forward(time_step=t) + + def test_variance(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5 + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def test_batch_step_no_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_trained_timesteps = len(scheduler) + + model = self.dummy_model() + sample1 = self.dummy_sample_deter + sample2 = self.dummy_sample_deter + 0.1 + sample3 = self.dummy_sample_deter - 0.1 + + per_sample_batch = sample1.shape[0] + samples = torch.stack([sample1, sample2, sample3], dim=0) + timesteps = torch.arange(num_trained_timesteps)[0:3, None].repeat(1, per_sample_batch) + + residual = model(samples.flatten(0, 1), timesteps.flatten(0, 1)) + pred_prev_sample = scheduler.batch_step_no_noise(residual, timesteps.flatten(0, 1), samples.flatten(0, 1)) + + result_sum = torch.sum(torch.abs(pred_prev_sample)) + result_mean = torch.mean(torch.abs(pred_prev_sample)) + + assert abs(result_sum.item() - 1153.1833) < 1e-2 + assert abs(result_mean.item() - 0.5005) < 1e-3 + + def test_full_loop_no_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_trained_timesteps = len(scheduler) + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(0) + + for t in reversed(range(num_trained_timesteps)): + # 1. predict noise residual + residual = model(sample, t) + + # 2. predict previous mean of sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 258.9606) < 1e-2 + assert abs(result_mean.item() - 0.3372) < 1e-3 + + def test_full_loop_with_v_prediction(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") + scheduler = scheduler_class(**scheduler_config) + + num_trained_timesteps = len(scheduler) + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(0) + + for t in reversed(range(num_trained_timesteps)): + # 1. predict noise residual + residual = model(sample, t) + + # 2. predict previous mean of sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 202.0296) < 1e-2 + assert abs(result_mean.item() - 0.2631) < 1e-3 + + def test_custom_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 1, 0] + + scheduler.set_timesteps(timesteps=timesteps) + + scheduler_timesteps = scheduler.timesteps + + for i, timestep in enumerate(scheduler_timesteps): + if i == len(timesteps) - 1: + expected_prev_t = -1 + else: + expected_prev_t = timesteps[i + 1] + + prev_t = scheduler.previous_timestep(timestep) + prev_t = prev_t.item() + + self.assertEqual(prev_t, expected_prev_t) + + def test_custom_timesteps_increasing_order(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 51, 0] + + with self.assertRaises(ValueError, msg="`custom_timesteps` must be in descending order."): + scheduler.set_timesteps(timesteps=timesteps) + + def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 1, 0] + num_inference_steps = len(timesteps) + + with self.assertRaises(ValueError, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."): + scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps) + + def test_custom_timesteps_too_large(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [scheduler.config.num_train_timesteps] + + with self.assertRaises( + ValueError, + msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}", + ): + scheduler.set_timesteps(timesteps=timesteps) + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_trained_timesteps = len(scheduler) + t_start = num_trained_timesteps - 2 + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(0) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for t in timesteps: + # 1. predict noise residual + residual = model(sample, t) + + # 2. predict previous mean of sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 387.9466) < 1e-2, f" expected result sum 387.9466, but get {result_sum}" + assert abs(result_mean.item() - 0.5051) < 1e-3, f" expected result mean 0.5051, but get {result_mean}" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_deis.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_deis.py new file mode 100644 index 0000000000000000000000000000000000000000..048bde51c36618221845dd48a642882208b77458 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_deis.py @@ -0,0 +1,273 @@ +import tempfile +import unittest + +import torch + +from diffusers import ( + DEISMultistepScheduler, + DPMSolverMultistepScheduler, + DPMSolverSinglestepScheduler, + UniPCMultistepScheduler, +) + +from .test_schedulers import SchedulerCommonTest + + +class DEISMultistepSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DEISMultistepScheduler,) + forward_default_kwargs = (("num_inference_steps", 25),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "solver_order": 2, + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output, new_output = sample, sample + for t in range(time_step, time_step + scheduler.config.solver_order + 1): + t = scheduler.timesteps[t] + output = scheduler.step(residual, t, output, **kwargs).prev_sample + new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + @unittest.skip("Test not supported.") + def test_from_save_pretrained(self): + pass + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, scheduler=None, **config): + if scheduler is None: + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + time_step_0 = scheduler.timesteps[5] + time_step_1 = scheduler.timesteps[6] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_switch(self): + # make sure that iterating over schedulers with same config names gives same results + # for defaults + scheduler = DEISMultistepScheduler(**self.get_scheduler_config()) + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.23916) < 1e-3 + + scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config) + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + scheduler = UniPCMultistepScheduler.from_config(scheduler.config) + scheduler = DEISMultistepScheduler.from_config(scheduler.config) + + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.23916) < 1e-3 + + def test_timesteps(self): + for timesteps in [25, 50, 100, 999, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for order in [1, 2, 3]: + for solver_type in ["logrho"]: + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + algorithm_type="deis", + solver_order=order, + solver_type=solver_type, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_solver_order_and_type(self): + for algorithm_type in ["deis"]: + for solver_type in ["logrho"]: + for order in [1, 2, 3]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + sample = self.full_loop( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + assert not torch.isnan(sample).any(), "Samples have nan numbers" + + def test_lower_order_final(self): + self.check_over_configs(lower_order_final=True) + self.check_over_configs(lower_order_final=False) + + def test_inference_steps(self): + for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]: + self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0) + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.23916) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.091) < 1e-3 + + def test_fp16_support(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter.half() + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + assert sample.dtype == torch.float16 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + t_start = 8 + + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 315.3016) < 1e-2, f" expected result sum 315.3016, but get {result_sum}" + assert abs(result_mean.item() - 0.41054) < 1e-3, f" expected result mean 0.41054, but get {result_mean}" + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_multi.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_multi.py new file mode 100644 index 0000000000000000000000000000000000000000..28c354709dc914f18347e7089ec7cb66c99f3174 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_multi.py @@ -0,0 +1,368 @@ +import tempfile +import unittest + +import torch + +from diffusers import ( + DEISMultistepScheduler, + DPMSolverMultistepScheduler, + DPMSolverSinglestepScheduler, + UniPCMultistepScheduler, +) + +from .test_schedulers import SchedulerCommonTest + + +class DPMSolverMultistepSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DPMSolverMultistepScheduler,) + forward_default_kwargs = (("num_inference_steps", 25),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "solver_order": 2, + "prediction_type": "epsilon", + "thresholding": False, + "sample_max_value": 1.0, + "algorithm_type": "dpmsolver++", + "solver_type": "midpoint", + "lower_order_final": False, + "euler_at_final": False, + "lambda_min_clipped": -float("inf"), + "variance_type": None, + "final_sigmas_type": "sigma_min", + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output, new_output = sample, sample + for t in range(time_step, time_step + scheduler.config.solver_order + 1): + t = new_scheduler.timesteps[t] + output = scheduler.step(residual, t, output, **kwargs).prev_sample + new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + @unittest.skip("Test not supported.") + def test_from_save_pretrained(self): + pass + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + time_step = new_scheduler.timesteps[time_step] + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, scheduler=None, **config): + if scheduler is None: + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + generator = torch.manual_seed(0) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + return sample + + def full_loop_custom_timesteps(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + scheduler.set_timesteps(num_inference_steps) + timesteps = scheduler.timesteps + # reset the timesteps using `timesteps` + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps) + + generator = torch.manual_seed(0) + model = self.dummy_model() + sample = self.dummy_sample_deter + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + return sample + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + time_step_0 = scheduler.timesteps[5] + time_step_1 = scheduler.timesteps[6] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_timesteps(self): + for timesteps in [25, 50, 100, 999, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for order in [1, 2, 3]: + for solver_type in ["midpoint", "heun"]: + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + algorithm_type="dpmsolver++", + solver_order=order, + solver_type=solver_type, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_solver_order_and_type(self): + for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]: + for solver_type in ["midpoint", "heun"]: + for order in [1, 2, 3]: + for prediction_type in ["epsilon", "sample"]: + if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: + if order == 3: + continue + else: + self.check_over_configs( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + sample = self.full_loop( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + assert not torch.isnan(sample).any(), "Samples have nan numbers" + + def test_lower_order_final(self): + self.check_over_configs(lower_order_final=True) + self.check_over_configs(lower_order_final=False) + + def test_euler_at_final(self): + self.check_over_configs(euler_at_final=True) + self.check_over_configs(euler_at_final=False) + + def test_lambda_min_clipped(self): + self.check_over_configs(lambda_min_clipped=-float("inf")) + self.check_over_configs(lambda_min_clipped=-5.1) + + def test_variance_type(self): + self.check_over_configs(variance_type=None) + self.check_over_configs(variance_type="learned_range") + + def test_inference_steps(self): + for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]: + self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0) + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.3301) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + t_start = 5 + + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 318.4111) < 1e-2, f" expected result sum 318.4111, but get {result_sum}" + assert abs(result_mean.item() - 0.4146) < 1e-3, f" expected result mean 0.4146, but get {result_mean}" + + def test_full_loop_no_noise_thres(self): + sample = self.full_loop(thresholding=True, dynamic_thresholding_ratio=0.87, sample_max_value=0.5) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 1.1364) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2251) < 1e-3 + + def test_full_loop_with_karras_and_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction", use_karras_sigmas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2096) < 1e-3 + + def test_full_loop_with_lu_and_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction", use_lu_lambdas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.1554) < 1e-3 + + def test_switch(self): + # make sure that iterating over schedulers with same config names gives same results + # for defaults + scheduler = DPMSolverMultistepScheduler(**self.get_scheduler_config()) + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.3301) < 1e-3 + + scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config) + scheduler = UniPCMultistepScheduler.from_config(scheduler.config) + scheduler = DEISMultistepScheduler.from_config(scheduler.config) + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.3301) < 1e-3 + + def test_fp16_support(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter.half() + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + assert sample.dtype == torch.float16 + + def test_duplicated_timesteps(self): + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(scheduler.config.num_train_timesteps) + assert len(scheduler.timesteps) == scheduler.num_inference_steps + + def test_custom_timesteps(self): + for algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: + for prediction_type in ["epsilon", "sample", "v_prediction"]: + for final_sigmas_type in ["sigma_min", "zero"]: + sample = self.full_loop( + algorithm_type=algorithm_type, + prediction_type=prediction_type, + final_sigmas_type=final_sigmas_type, + ) + sample_custom_timesteps = self.full_loop_custom_timesteps( + algorithm_type=algorithm_type, + prediction_type=prediction_type, + final_sigmas_type=final_sigmas_type, + ) + assert torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5, ( + f"Scheduler outputs are not identical for algorithm_type: {algorithm_type}, prediction_type: {prediction_type} and final_sigmas_type: {final_sigmas_type}" + ) + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_multi_inverse.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_multi_inverse.py new file mode 100644 index 0000000000000000000000000000000000000000..0eced957190ca46c14877827bb410cc7b84ed6ae --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_multi_inverse.py @@ -0,0 +1,273 @@ +import tempfile + +import torch + +from diffusers import DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler + +from .test_schedulers import SchedulerCommonTest + + +class DPMSolverMultistepSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DPMSolverMultistepInverseScheduler,) + forward_default_kwargs = (("num_inference_steps", 25),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "solver_order": 2, + "prediction_type": "epsilon", + "thresholding": False, + "sample_max_value": 1.0, + "algorithm_type": "dpmsolver++", + "solver_type": "midpoint", + "lower_order_final": False, + "lambda_min_clipped": -float("inf"), + "variance_type": None, + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output, new_output = sample, sample + for t in range(time_step, time_step + scheduler.config.solver_order + 1): + t = scheduler.timesteps[t] + output = scheduler.step(residual, t, output, **kwargs).prev_sample + new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def test_from_save_pretrained(self): + pass + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, scheduler=None, **config): + if scheduler is None: + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + time_step_0 = scheduler.timesteps[5] + time_step_1 = scheduler.timesteps[6] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_timesteps(self): + for timesteps in [25, 50, 100, 999, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for order in [1, 2, 3]: + for solver_type in ["midpoint", "heun"]: + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + algorithm_type="dpmsolver++", + solver_order=order, + solver_type=solver_type, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_solver_order_and_type(self): + for algorithm_type in ["dpmsolver", "dpmsolver++"]: + for solver_type in ["midpoint", "heun"]: + for order in [1, 2, 3]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + sample = self.full_loop( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + assert not torch.isnan(sample).any(), "Samples have nan numbers" + + def test_lower_order_final(self): + self.check_over_configs(lower_order_final=True) + self.check_over_configs(lower_order_final=False) + + def test_lambda_min_clipped(self): + self.check_over_configs(lambda_min_clipped=-float("inf")) + self.check_over_configs(lambda_min_clipped=-5.1) + + def test_variance_type(self): + self.check_over_configs(variance_type=None) + self.check_over_configs(variance_type="learned_range") + + def test_timestep_spacing(self): + for timestep_spacing in ["trailing", "leading"]: + self.check_over_configs(timestep_spacing=timestep_spacing) + + def test_inference_steps(self): + for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]: + self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0) + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.7047) < 1e-3 + + def test_full_loop_no_noise_thres(self): + sample = self.full_loop(thresholding=True, dynamic_thresholding_ratio=0.87, sample_max_value=0.5) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 19.8933) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 1.5194) < 1e-3 + + def test_full_loop_with_karras_and_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction", use_karras_sigmas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 1.7833) < 2e-3 + + def test_switch(self): + # make sure that iterating over schedulers with same config names gives same results + # for defaults + scheduler = DPMSolverMultistepInverseScheduler(**self.get_scheduler_config()) + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.7047) < 1e-3 + + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + scheduler = DPMSolverMultistepInverseScheduler.from_config(scheduler.config) + + sample = self.full_loop(scheduler=scheduler) + new_result_mean = torch.mean(torch.abs(sample)) + + assert abs(new_result_mean.item() - result_mean.item()) < 1e-3 + + def test_fp16_support(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter.half() + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + assert sample.dtype == torch.float16 + + def test_unique_timesteps(self, **config): + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(scheduler.config.num_train_timesteps) + assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_sde.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_sde.py new file mode 100644 index 0000000000000000000000000000000000000000..e4dde67344ac7a317b4110ea511b590c88442bd7 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_sde.py @@ -0,0 +1,173 @@ +import torch + +from diffusers import DPMSolverSDEScheduler + +from ..testing_utils import require_torchsde, torch_device +from .test_schedulers import SchedulerCommonTest + + +@require_torchsde +class DPMSolverSDESchedulerTest(SchedulerCommonTest): + scheduler_classes = (DPMSolverSDEScheduler,) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1100, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "noise_sampler_seed": 0, + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_full_loop_no_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["mps"]: + assert abs(result_sum.item() - 167.47821044921875) < 1e-2 + assert abs(result_mean.item() - 0.2178705964565277) < 1e-3 + elif torch_device in ["cuda", "xpu"]: + assert abs(result_sum.item() - 171.59352111816406) < 1e-2 + assert abs(result_mean.item() - 0.22342906892299652) < 1e-3 + else: + assert abs(result_sum.item() - 162.52383422851562) < 1e-2 + assert abs(result_mean.item() - 0.211619570851326) < 1e-3 + + def test_full_loop_with_v_prediction(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["mps"]: + assert abs(result_sum.item() - 124.77149200439453) < 1e-2 + assert abs(result_mean.item() - 0.16226289014816284) < 1e-3 + elif torch_device in ["cuda", "xpu"]: + assert abs(result_sum.item() - 128.1663360595703) < 1e-2 + assert abs(result_mean.item() - 0.16688326001167297) < 1e-3 + else: + assert abs(result_sum.item() - 119.8487548828125) < 1e-2 + assert abs(result_mean.item() - 0.1560530662536621) < 1e-3 + + def test_full_loop_device(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["mps"]: + assert abs(result_sum.item() - 167.46957397460938) < 1e-2 + assert abs(result_mean.item() - 0.21805934607982635) < 1e-3 + elif torch_device in ["cuda", "xpu"]: + assert abs(result_sum.item() - 171.59353637695312) < 1e-2 + assert abs(result_mean.item() - 0.22342908382415771) < 1e-3 + else: + assert abs(result_sum.item() - 162.52383422851562) < 1e-2 + assert abs(result_mean.item() - 0.211619570851326) < 1e-3 + + def test_full_loop_device_karras_sigmas(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["mps"]: + assert abs(result_sum.item() - 176.66974135742188) < 1e-2 + assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 + elif torch_device in ["cuda", "xpu"]: + assert abs(result_sum.item() - 177.63653564453125) < 1e-2 + assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 + else: + assert abs(result_sum.item() - 170.3135223388672) < 1e-2 + assert abs(result_mean.item() - 0.23003872730981811) < 1e-2 + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_single.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_single.py new file mode 100644 index 0000000000000000000000000000000000000000..0756a5ed71ff97d0b25363643b77404a6785d936 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_dpm_single.py @@ -0,0 +1,356 @@ +import tempfile +import unittest + +import torch + +from diffusers import ( + DEISMultistepScheduler, + DPMSolverMultistepScheduler, + DPMSolverSinglestepScheduler, + UniPCMultistepScheduler, +) + +from .test_schedulers import SchedulerCommonTest + + +class DPMSolverSinglestepSchedulerTest(SchedulerCommonTest): + scheduler_classes = (DPMSolverSinglestepScheduler,) + forward_default_kwargs = (("num_inference_steps", 25),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "solver_order": 2, + "prediction_type": "epsilon", + "thresholding": False, + "sample_max_value": 1.0, + "algorithm_type": "dpmsolver++", + "solver_type": "midpoint", + "lambda_min_clipped": -float("inf"), + "variance_type": None, + "final_sigmas_type": "sigma_min", + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output, new_output = sample, sample + for t in range(time_step, time_step + scheduler.config.solver_order + 1): + t = scheduler.timesteps[t] + output = scheduler.step(residual, t, output, **kwargs).prev_sample + new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + @unittest.skip("Test not supported.") + def test_from_save_pretrained(self): + pass + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, scheduler=None, **config): + if scheduler is None: + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def full_loop_custom_timesteps(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + scheduler.set_timesteps(num_inference_steps) + timesteps = scheduler.timesteps + # reset the timesteps using`timesteps` + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps) + + model = self.dummy_model() + sample = self.dummy_sample_deter + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def test_full_uneven_loop(self): + scheduler = DPMSolverSinglestepScheduler(**self.get_scheduler_config()) + num_inference_steps = 50 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + # make sure that the first t is uneven + for i, t in enumerate(scheduler.timesteps[3:]): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2574) < 1e-3 + + def test_timesteps(self): + for timesteps in [25, 50, 100, 999, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_switch(self): + # make sure that iterating over schedulers with same config names gives same results + # for defaults + scheduler = DPMSolverSinglestepScheduler(**self.get_scheduler_config()) + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2791) < 1e-3 + + scheduler = DEISMultistepScheduler.from_config(scheduler.config) + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + scheduler = UniPCMultistepScheduler.from_config(scheduler.config) + scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config) + + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2791) < 1e-3 + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for order in [1, 2, 3]: + for solver_type in ["midpoint", "heun"]: + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + algorithm_type="dpmsolver++", + solver_order=order, + solver_type=solver_type, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_solver_order_and_type(self): + for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver++"]: + for solver_type in ["midpoint", "heun"]: + for order in [1, 2, 3]: + for prediction_type in ["epsilon", "sample"]: + if algorithm_type == "sde-dpmsolver++": + if order == 3: + continue + else: + self.check_over_configs( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + sample = self.full_loop( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + assert not torch.isnan(sample).any(), "Samples have nan numbers" + + def test_lower_order_final(self): + self.check_over_configs(lower_order_final=True) + self.check_over_configs(lower_order_final=False) + + def test_lambda_min_clipped(self): + self.check_over_configs(lambda_min_clipped=-float("inf")) + self.check_over_configs(lambda_min_clipped=-5.1) + + def test_variance_type(self): + self.check_over_configs(variance_type=None) + self.check_over_configs(variance_type="learned_range") + + def test_inference_steps(self): + for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]: + self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0) + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2791) < 1e-3 + + def test_full_loop_with_karras(self): + sample = self.full_loop(use_karras_sigmas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2248) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.1453) < 1e-3 + + def test_full_loop_with_karras_and_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction", use_karras_sigmas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.0649) < 1e-3 + + def test_fp16_support(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter.half() + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + assert sample.dtype == torch.float16 + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + time_step_0 = scheduler.timesteps[0] + time_step_1 = scheduler.timesteps[1] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + t_start = 5 + + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 269.2187) < 1e-2, f" expected result sum 269.2187, but get {result_sum}" + assert abs(result_mean.item() - 0.3505) < 1e-3, f" expected result mean 0.3505, but get {result_mean}" + + def test_custom_timesteps(self): + for prediction_type in ["epsilon", "sample", "v_prediction"]: + for lower_order_final in [True, False]: + for final_sigmas_type in ["sigma_min", "zero"]: + sample = self.full_loop( + prediction_type=prediction_type, + lower_order_final=lower_order_final, + final_sigmas_type=final_sigmas_type, + ) + sample_custom_timesteps = self.full_loop_custom_timesteps( + prediction_type=prediction_type, + lower_order_final=lower_order_final, + final_sigmas_type=final_sigmas_type, + ) + assert torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5, ( + f"Scheduler outputs are not identical for prediction_type: {prediction_type}, lower_order_final: {lower_order_final} and final_sigmas_type: {final_sigmas_type}" + ) + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_edm_dpmsolver_multistep.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_edm_dpmsolver_multistep.py new file mode 100644 index 0000000000000000000000000000000000000000..8525ce61c40d2670d03d7e04b075fb8236a505cc --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_edm_dpmsolver_multistep.py @@ -0,0 +1,262 @@ +import tempfile +import unittest + +import torch + +from diffusers import EDMDPMSolverMultistepScheduler + +from .test_schedulers import SchedulerCommonTest + + +class EDMDPMSolverMultistepSchedulerTest(SchedulerCommonTest): + scheduler_classes = (EDMDPMSolverMultistepScheduler,) + forward_default_kwargs = (("num_inference_steps", 25),) + + def get_scheduler_config(self, **kwargs): + config = { + "sigma_min": 0.002, + "sigma_max": 80.0, + "sigma_data": 0.5, + "num_train_timesteps": 1000, + "solver_order": 2, + "prediction_type": "epsilon", + "thresholding": False, + "sample_max_value": 1.0, + "algorithm_type": "dpmsolver++", + "solver_type": "midpoint", + "lower_order_final": False, + "euler_at_final": False, + "final_sigmas_type": "sigma_min", + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output, new_output = sample, sample + for t in range(time_step, time_step + scheduler.config.solver_order + 1): + t = new_scheduler.timesteps[t] + output = scheduler.step(residual, t, output, **kwargs).prev_sample + new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + @unittest.skip("Test not supported.") + def test_from_save_pretrained(self): + pass + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + time_step = new_scheduler.timesteps[time_step] + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, scheduler=None, **config): + if scheduler is None: + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + time_step_0 = scheduler.timesteps[5] + time_step_1 = scheduler.timesteps[6] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_timesteps(self): + for timesteps in [25, 50, 100, 999, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for order in [1, 2, 3]: + for solver_type in ["midpoint", "heun"]: + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + algorithm_type="dpmsolver++", + solver_order=order, + solver_type=solver_type, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + # TODO (patil-suraj): Fix this test + @unittest.skip("Skip for now, as it failing currently but works with the actual model") + def test_solver_order_and_type(self): + for algorithm_type in ["dpmsolver++", "sde-dpmsolver++"]: + for solver_type in ["midpoint", "heun"]: + for order in [1, 2, 3]: + for prediction_type in ["epsilon", "v_prediction"]: + if algorithm_type == "sde-dpmsolver++": + if order == 3: + continue + else: + self.check_over_configs( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + sample = self.full_loop( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + algorithm_type=algorithm_type, + ) + assert not torch.isnan(sample).any(), ( + f"Samples have nan numbers, {order}, {solver_type}, {prediction_type}, {algorithm_type}" + ) + + def test_lower_order_final(self): + self.check_over_configs(lower_order_final=True) + self.check_over_configs(lower_order_final=False) + + def test_euler_at_final(self): + self.check_over_configs(euler_at_final=True) + self.check_over_configs(euler_at_final=False) + + def test_inference_steps(self): + for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]: + self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0) + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.0001) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + t_start = 5 + + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 8.1661) < 1e-2, f" expected result sum 8.1661, but get {result_sum}" + assert abs(result_mean.item() - 0.0106) < 1e-3, f" expected result mean 0.0106, but get {result_mean}" + + def test_full_loop_no_noise_thres(self): + sample = self.full_loop(thresholding=True, dynamic_thresholding_ratio=0.87, sample_max_value=0.5) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.0080) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.0092) < 1e-3 + + def test_duplicated_timesteps(self, **config): + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(scheduler.config.num_train_timesteps) + assert len(scheduler.timesteps) == scheduler.num_inference_steps + + @unittest.skip("Test not supported.") + def test_trained_betas(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_edm_euler.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_edm_euler.py new file mode 100644 index 0000000000000000000000000000000000000000..acac4b1f4caed2aff6741a40e9214adedc0f744f --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_edm_euler.py @@ -0,0 +1,203 @@ +import inspect +import tempfile +import unittest +from typing import Dict, List, Tuple + +import torch + +from diffusers import EDMEulerScheduler + +from .test_schedulers import SchedulerCommonTest + + +class EDMEulerSchedulerTest(SchedulerCommonTest): + scheduler_classes = (EDMEulerScheduler,) + forward_default_kwargs = (("num_inference_steps", 10),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 256, + "sigma_min": 0.002, + "sigma_max": 80.0, + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_full_loop_no_noise(self, num_inference_steps=10, seed=0): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + for i, t in enumerate(scheduler.timesteps): + scaled_sample = scheduler.scale_model_input(sample, t) + + model_output = model(scaled_sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 34.1855) < 1e-3 + assert abs(result_mean.item() - 0.044) < 1e-3 + + def test_full_loop_device(self, num_inference_steps=10, seed=0): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + for i, t in enumerate(scheduler.timesteps): + scaled_sample = scheduler.scale_model_input(sample, t) + + model_output = model(scaled_sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 34.1855) < 1e-3 + assert abs(result_mean.item() - 0.044) < 1e-3 + + # Override test_from_save_pretrained to use EDMEulerScheduler-specific logic + def test_from_save_pretrained(self): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + scheduler.set_timesteps(num_inference_steps) + new_scheduler.set_timesteps(num_inference_steps) + timestep = scheduler.timesteps[0] + + sample = self.dummy_sample + + scaled_sample = scheduler.scale_model_input(sample, timestep) + residual = 0.1 * scaled_sample + + new_scaled_sample = new_scheduler.scale_model_input(sample, timestep) + new_residual = 0.1 * new_scaled_sample + + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample + + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + new_output = new_scheduler.step(new_residual, timestep, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + # Override test_from_save_pretrained to use EDMEulerScheduler-specific logic + def test_step_shape(self): + num_inference_steps = 10 + + scheduler_config = self.get_scheduler_config() + scheduler = self.scheduler_classes[0](**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + + timestep_0 = scheduler.timesteps[0] + timestep_1 = scheduler.timesteps[1] + + sample = self.dummy_sample + scaled_sample = scheduler.scale_model_input(sample, timestep_0) + residual = 0.1 * scaled_sample + + output_0 = scheduler.step(residual, timestep_0, sample).prev_sample + output_1 = scheduler.step(residual, timestep_1, sample).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + # Override test_from_save_pretrained to use EDMEulerScheduler-specific logic + def test_scheduler_outputs_equivalence(self): + def set_nan_tensor_to_zero(t): + t[t != t] = 0 + return t + + def recursive_check(tuple_object, dict_object): + if isinstance(tuple_object, (List, Tuple)): + for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()): + recursive_check(tuple_iterable_value, dict_iterable_value) + elif isinstance(tuple_object, Dict): + for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()): + recursive_check(tuple_iterable_value, dict_iterable_value) + elif tuple_object is None: + return + else: + self.assertTrue( + torch.allclose( + set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 + ), + msg=( + "Tuple and dict output are not equal. Difference:" + f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" + f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" + f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." + ), + ) + + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", 50) + + timestep = 0 + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + timestep = scheduler.timesteps[0] + + sample = self.dummy_sample + scaled_sample = scheduler.scale_model_input(sample, timestep) + residual = 0.1 * scaled_sample + + # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + outputs_dict = scheduler.step(residual, timestep, sample, **kwargs) + + scheduler.set_timesteps(num_inference_steps) + + scaled_sample = scheduler.scale_model_input(sample, timestep) + residual = 0.1 * scaled_sample + + # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs) + + recursive_check(outputs_tuple, outputs_dict) + + @unittest.skip(reason="EDMEulerScheduler does not support beta schedules.") + def test_trained_betas(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_euler.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_euler.py new file mode 100644 index 0000000000000000000000000000000000000000..ee99465abfc39e7a7ce7a6734b22dbeb42df5efd --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_euler.py @@ -0,0 +1,271 @@ +import torch + +from diffusers import EulerDiscreteScheduler + +from ..testing_utils import torch_device +from .test_schedulers import SchedulerCommonTest + + +class EulerDiscreteSchedulerTest(SchedulerCommonTest): + scheduler_classes = (EulerDiscreteScheduler,) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1100, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_timestep_type(self): + timestep_types = ["discrete", "continuous"] + for timestep_type in timestep_types: + self.check_over_configs(timestep_type=timestep_type) + + def test_karras_sigmas(self): + self.check_over_configs(use_karras_sigmas=True, sigma_min=0.02, sigma_max=700.0) + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def full_loop(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = self.num_inference_steps + scheduler.set_timesteps(num_inference_steps) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + return sample + + def full_loop_custom_timesteps(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = self.num_inference_steps + scheduler.set_timesteps(num_inference_steps) + timesteps = scheduler.timesteps + # reset the timesteps using `timesteps` + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + return sample + + def full_loop_custom_sigmas(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = self.num_inference_steps + scheduler.set_timesteps(num_inference_steps) + sigmas = scheduler.sigmas + # reset the timesteps using `sigmas` + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps=None, sigmas=sigmas) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + return sample + + def test_full_loop_no_noise(self): + sample = self.full_loop() + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 10.0807) < 1e-2 + assert abs(result_mean.item() - 0.0131) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 0.0002) < 1e-2 + assert abs(result_mean.item() - 2.2676e-06) < 1e-3 + + def test_full_loop_device(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() + sample = sample.to(torch_device) + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 10.0807) < 1e-2 + assert abs(result_mean.item() - 0.0131) < 1e-3 + + def test_full_loop_device_karras_sigmas(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() + sample = sample.to(torch_device) + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 124.52299499511719) < 1e-2 + assert abs(result_mean.item() - 0.16213932633399963) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + # add noise + t_start = self.num_inference_steps - 2 + noise = self.dummy_noise_deter + noise = noise.to(sample.device) + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 57062.9297) < 1e-2, f" expected result sum 57062.9297, but get {result_sum}" + assert abs(result_mean.item() - 74.3007) < 1e-3, f" expected result mean 74.3007, but get {result_mean}" + + def test_custom_timesteps(self): + for prediction_type in ["epsilon", "sample", "v_prediction"]: + for interpolation_type in ["linear", "log_linear"]: + for final_sigmas_type in ["sigma_min", "zero"]: + sample = self.full_loop( + prediction_type=prediction_type, + interpolation_type=interpolation_type, + final_sigmas_type=final_sigmas_type, + ) + sample_custom_timesteps = self.full_loop_custom_timesteps( + prediction_type=prediction_type, + interpolation_type=interpolation_type, + final_sigmas_type=final_sigmas_type, + ) + assert torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5, ( + f"Scheduler outputs are not identical for prediction_type: {prediction_type}, interpolation_type: {interpolation_type} and final_sigmas_type: {final_sigmas_type}" + ) + + def test_custom_sigmas(self): + for prediction_type in ["epsilon", "sample", "v_prediction"]: + for final_sigmas_type in ["sigma_min", "zero"]: + sample = self.full_loop( + prediction_type=prediction_type, + final_sigmas_type=final_sigmas_type, + ) + sample_custom_timesteps = self.full_loop_custom_sigmas( + prediction_type=prediction_type, + final_sigmas_type=final_sigmas_type, + ) + assert torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5, ( + f"Scheduler outputs are not identical for prediction_type: {prediction_type} and final_sigmas_type: {final_sigmas_type}" + ) + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_euler_ancestral.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_euler_ancestral.py new file mode 100644 index 0000000000000000000000000000000000000000..c4fe61bfc387ba1361800f6d660e761b9fdef7fd --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_euler_ancestral.py @@ -0,0 +1,156 @@ +import torch + +from diffusers import EulerAncestralDiscreteScheduler + +from ..testing_utils import torch_device +from .test_schedulers import SchedulerCommonTest + + +class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest): + scheduler_classes = (EulerAncestralDiscreteScheduler,) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1100, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def test_full_loop_no_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 152.3192) < 1e-2 + assert abs(result_mean.item() - 0.1983) < 1e-3 + + def test_full_loop_with_v_prediction(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 108.4439) < 1e-2 + assert abs(result_mean.item() - 0.1412) < 1e-3 + + def test_full_loop_device(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() + sample = sample.to(torch_device) + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 152.3192) < 1e-2 + assert abs(result_mean.item() - 0.1983) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + t_start = self.num_inference_steps - 2 + + scheduler.set_timesteps(self.num_inference_steps) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + # add noise + noise = self.dummy_noise_deter + noise = noise.to(sample.device) + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 56163.0508) < 1e-2, f" expected result sum 56163.0508, but get {result_sum}" + assert abs(result_mean.item() - 73.1290) < 1e-3, f" expected result mean 73.1290, but get {result_mean}" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_heun.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_heun.py new file mode 100644 index 0000000000000000000000000000000000000000..97bef50048ba2011c106df5987af7368c033ee5a --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_heun.py @@ -0,0 +1,227 @@ +import torch + +from diffusers import HeunDiscreteScheduler + +from ..testing_utils import torch_device +from .test_schedulers import SchedulerCommonTest + + +class HeunDiscreteSchedulerTest(SchedulerCommonTest): + scheduler_classes = (HeunDiscreteScheduler,) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1100, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear", "exp"]: + self.check_over_configs(beta_schedule=schedule) + + def test_clip_sample(self): + for clip_sample_range in [1.0, 2.0, 3.0]: + self.check_over_configs(clip_sample_range=clip_sample_range, clip_sample=True) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction", "sample"]: + self.check_over_configs(prediction_type=prediction_type) + + def full_loop(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = self.num_inference_steps + scheduler.set_timesteps(num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + return sample + + def full_loop_custom_timesteps(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = self.num_inference_steps + scheduler.set_timesteps(num_inference_steps) + timesteps = scheduler.timesteps + timesteps = torch.cat([timesteps[:1], timesteps[1::2]]) + # reset the timesteps using `timesteps` + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps=None, timesteps=timesteps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + return sample + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["cpu", "mps"]: + assert abs(result_sum.item() - 0.1233) < 1e-2 + assert abs(result_mean.item() - 0.0002) < 1e-3 + else: + # CUDA + assert abs(result_sum.item() - 0.1233) < 1e-2 + assert abs(result_mean.item() - 0.0002) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["cpu", "mps"]: + assert abs(result_sum.item() - 4.6934e-07) < 1e-2 + assert abs(result_mean.item() - 6.1112e-10) < 1e-3 + else: + # CUDA + assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2 + assert abs(result_mean.item() - 0.0002) < 1e-3 + + def test_full_loop_device(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if str(torch_device).startswith("cpu"): + # The following sum varies between 148 and 156 on mps. Why? + assert abs(result_sum.item() - 0.1233) < 1e-2 + assert abs(result_mean.item() - 0.0002) < 1e-3 + elif str(torch_device).startswith("mps"): + # Larger tolerance on mps + assert abs(result_mean.item() - 0.0002) < 1e-2 + else: + # CUDA + assert abs(result_sum.item() - 0.1233) < 1e-2 + assert abs(result_mean.item() - 0.0002) < 1e-3 + + def test_full_loop_device_karras_sigmas(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 0.00015) < 1e-2 + assert abs(result_mean.item() - 1.9869554535034695e-07) < 1e-2 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + t_start = self.num_inference_steps - 2 + noise = self.dummy_noise_deter + noise = noise.to(torch_device) + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 75074.8906) < 1e-2, f" expected result sum 75074.8906, but get {result_sum}" + assert abs(result_mean.item() - 97.7538) < 1e-3, f" expected result mean 97.7538, but get {result_mean}" + + def test_custom_timesteps(self): + for prediction_type in ["epsilon", "sample", "v_prediction"]: + for timestep_spacing in ["linspace", "leading"]: + sample = self.full_loop( + prediction_type=prediction_type, + timestep_spacing=timestep_spacing, + ) + sample_custom_timesteps = self.full_loop_custom_timesteps( + prediction_type=prediction_type, + timestep_spacing=timestep_spacing, + ) + assert torch.sum(torch.abs(sample - sample_custom_timesteps)) < 1e-5, ( + f"Scheduler outputs are not identical for prediction_type: {prediction_type}, timestep_spacing: {timestep_spacing}" + ) + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ipndm.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ipndm.py new file mode 100644 index 0000000000000000000000000000000000000000..ac7973c582950816039e96e1ef4aad245d4cd3f1 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_ipndm.py @@ -0,0 +1,165 @@ +import tempfile +import unittest + +import torch + +from diffusers import IPNDMScheduler + +from .test_schedulers import SchedulerCommonTest + + +class IPNDMSchedulerTest(SchedulerCommonTest): + scheduler_classes = (IPNDMScheduler,) + forward_default_kwargs = (("num_inference_steps", 50),) + + def get_scheduler_config(self, **kwargs): + config = {"num_train_timesteps": 1000} + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.ets = dummy_past_residuals[:] + + if time_step is None: + time_step = scheduler.timesteps[len(scheduler.timesteps) // 2] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.ets = dummy_past_residuals[:] + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + @unittest.skip("Test not supported.") + def test_from_save_pretrained(self): + pass + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.ets = dummy_past_residuals[:] + + if time_step is None: + time_step = scheduler.timesteps[len(scheduler.timesteps) // 2] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.ets = dummy_past_residuals[:] + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + scheduler._step_index = None + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] + scheduler.ets = dummy_past_residuals[:] + + time_step_0 = scheduler.timesteps[5] + time_step_1 = scheduler.timesteps[6] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_timesteps(self): + for timesteps in [100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps, time_step=None) + + def test_inference_steps(self): + for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]): + self.check_over_forward(num_inference_steps=num_inference_steps, time_step=None) + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 2540529) < 10 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_kdpm2_ancestral.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_kdpm2_ancestral.py new file mode 100644 index 0000000000000000000000000000000000000000..135534db45361503fa5a7965e7a4c2640db33247 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_kdpm2_ancestral.py @@ -0,0 +1,164 @@ +import torch + +from diffusers import KDPM2AncestralDiscreteScheduler + +from ..testing_utils import torch_device +from .test_schedulers import SchedulerCommonTest + + +class KDPM2AncestralDiscreteSchedulerTest(SchedulerCommonTest): + scheduler_classes = (KDPM2AncestralDiscreteScheduler,) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1100, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear"]: + self.check_over_configs(beta_schedule=schedule) + + def test_full_loop_no_noise(self): + if torch_device == "mps": + return + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 13979.9433) < 1e-2 + assert abs(result_mean.item() - 18.2030) < 5e-3 + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_full_loop_with_v_prediction(self): + if torch_device == "mps": + return + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + generator = torch.manual_seed(0) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 331.8133) < 1e-2 + assert abs(result_mean.item() - 0.4320) < 1e-3 + + def test_full_loop_device(self): + if torch_device == "mps": + return + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 13979.9433) < 1e-1 + assert abs(result_mean.item() - 18.2030) < 1e-3 + + def test_full_loop_with_noise(self): + if torch_device == "mps": + return + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + generator = torch.manual_seed(0) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + # add noise + t_start = self.num_inference_steps - 2 + noise = self.dummy_noise_deter + noise = noise.to(sample.device) + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 93087.3437) < 1e-2, f" expected result sum 93087.3437, but get {result_sum}" + assert abs(result_mean.item() - 121.2074) < 5e-3, f" expected result mean 121.2074, but get {result_mean}" + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_kdpm2_discrete.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_kdpm2_discrete.py new file mode 100644 index 0000000000000000000000000000000000000000..370ba2253ee28ecd06ceaf911b2947343a9ee318 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_kdpm2_discrete.py @@ -0,0 +1,172 @@ +import torch + +from diffusers import KDPM2DiscreteScheduler + +from ..testing_utils import torch_device +from .test_schedulers import SchedulerCommonTest + + +class KDPM2DiscreteSchedulerTest(SchedulerCommonTest): + scheduler_classes = (KDPM2DiscreteScheduler,) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1100, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_full_loop_with_v_prediction(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["cpu", "mps"]: + assert abs(result_sum.item() - 4.6934e-07) < 1e-2 + assert abs(result_mean.item() - 6.1112e-10) < 1e-3 + else: + # CUDA + assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2 + assert abs(result_mean.item() - 0.0002) < 1e-3 + + def test_full_loop_no_noise(self): + if torch_device == "mps": + return + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["cpu", "mps"]: + assert abs(result_sum.item() - 20.4125) < 1e-2 + assert abs(result_mean.item() - 0.0266) < 1e-3 + else: + # CUDA + assert abs(result_sum.item() - 20.4125) < 1e-2 + assert abs(result_mean.item() - 0.0266) < 1e-3 + + def test_full_loop_device(self): + if torch_device == "mps": + return + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if str(torch_device).startswith("cpu"): + # The following sum varies between 148 and 156 on mps. Why? + assert abs(result_sum.item() - 20.4125) < 1e-2 + assert abs(result_mean.item() - 0.0266) < 1e-3 + else: + # CUDA + assert abs(result_sum.item() - 20.4125) < 1e-2 + assert abs(result_mean.item() - 0.0266) < 1e-3 + + def test_full_loop_with_noise(self): + if torch_device == "mps": + return + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + # add noise + t_start = self.num_inference_steps - 2 + noise = self.dummy_noise_deter + noise = noise.to(sample.device) + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 70408.4062) < 1e-2, f" expected result sum 70408.4062, but get {result_sum}" + assert abs(result_mean.item() - 91.6776) < 1e-3, f" expected result mean 91.6776, but get {result_mean}" + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_lcm.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_lcm.py new file mode 100644 index 0000000000000000000000000000000000000000..f54970e0eba33baa0cbdc7d868a339c0f60bf189 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_lcm.py @@ -0,0 +1,300 @@ +import tempfile +from typing import Dict, List, Tuple + +import torch + +from diffusers import LCMScheduler + +from ..testing_utils import torch_device +from .test_schedulers import SchedulerCommonTest + + +class LCMSchedulerTest(SchedulerCommonTest): + scheduler_classes = (LCMScheduler,) + forward_default_kwargs = (("num_inference_steps", 10),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.00085, + "beta_end": 0.0120, + "beta_schedule": "scaled_linear", + "prediction_type": "epsilon", + } + + config.update(**kwargs) + return config + + @property + def default_valid_timestep(self): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + + scheduler_config = self.get_scheduler_config() + scheduler = self.scheduler_classes[0](**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + timestep = scheduler.timesteps[-1] + return timestep + + def test_timesteps(self): + for timesteps in [100, 500, 1000]: + # 0 is not guaranteed to be in the timestep schedule, but timesteps - 1 is + self.check_over_configs(time_step=timesteps - 1, num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): + self.check_over_configs(time_step=self.default_valid_timestep, beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear", "squaredcos_cap_v2"]: + self.check_over_configs(time_step=self.default_valid_timestep, beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(time_step=self.default_valid_timestep, prediction_type=prediction_type) + + def test_clip_sample(self): + for clip_sample in [True, False]: + self.check_over_configs(time_step=self.default_valid_timestep, clip_sample=clip_sample) + + def test_thresholding(self): + self.check_over_configs(time_step=self.default_valid_timestep, thresholding=False) + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs( + time_step=self.default_valid_timestep, + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + ) + + def test_time_indices(self): + # Get default timestep schedule. + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + + scheduler_config = self.get_scheduler_config() + scheduler = self.scheduler_classes[0](**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + timesteps = scheduler.timesteps + for t in timesteps: + self.check_over_forward(time_step=t) + + def test_inference_steps(self): + # Hardcoded for now + for t, num_inference_steps in zip([99, 39, 39, 19], [10, 25, 26, 50]): + self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) + + # Override test_add_noise_device because the hardcoded num_inference_steps of 100 doesn't work + # for LCMScheduler under default settings + def test_add_noise_device(self, num_inference_steps=10): + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + sample = self.dummy_sample.to(torch_device) + scaled_sample = scheduler.scale_model_input(sample, 0.0) + self.assertEqual(sample.shape, scaled_sample.shape) + + noise = torch.randn(scaled_sample.shape).to(torch_device) + t = scheduler.timesteps[5][None] + noised = scheduler.add_noise(scaled_sample, noise, t) + self.assertEqual(noised.shape, scaled_sample.shape) + + # Override test_from_save_pretrained because it hardcodes a timestep of 1 + def test_from_save_pretrained(self): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + timestep = self.default_valid_timestep + + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + scheduler.set_timesteps(num_inference_steps) + new_scheduler.set_timesteps(num_inference_steps) + + kwargs["generator"] = torch.manual_seed(0) + output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample + + kwargs["generator"] = torch.manual_seed(0) + new_output = new_scheduler.step(residual, timestep, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + # Override test_step_shape because uses 0 and 1 as hardcoded timesteps + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + scheduler.set_timesteps(num_inference_steps) + + timestep_0 = scheduler.timesteps[-2] + timestep_1 = scheduler.timesteps[-1] + + output_0 = scheduler.step(residual, timestep_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, timestep_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + # Override test_set_scheduler_outputs_equivalence since it uses 0 as a hardcoded timestep + def test_scheduler_outputs_equivalence(self): + def set_nan_tensor_to_zero(t): + t[t != t] = 0 + return t + + def recursive_check(tuple_object, dict_object): + if isinstance(tuple_object, (List, Tuple)): + for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()): + recursive_check(tuple_iterable_value, dict_iterable_value) + elif isinstance(tuple_object, Dict): + for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()): + recursive_check(tuple_iterable_value, dict_iterable_value) + elif tuple_object is None: + return + else: + self.assertTrue( + torch.allclose( + set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 + ), + msg=( + "Tuple and dict output are not equal. Difference:" + f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" + f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" + f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." + ), + ) + + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", 50) + + timestep = self.default_valid_timestep + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + scheduler.set_timesteps(num_inference_steps) + kwargs["generator"] = torch.manual_seed(0) + outputs_dict = scheduler.step(residual, timestep, sample, **kwargs) + + scheduler.set_timesteps(num_inference_steps) + kwargs["generator"] = torch.manual_seed(0) + outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs) + + recursive_check(outputs_tuple, outputs_dict) + + def full_loop(self, num_inference_steps=10, seed=0, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(seed) + + scheduler.set_timesteps(num_inference_steps) + + for t in scheduler.timesteps: + residual = model(sample, t) + sample = scheduler.step(residual, t, sample, generator).prev_sample + + return sample + + def test_full_loop_onestep(self): + sample = self.full_loop(num_inference_steps=1) + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + # TODO: get expected sum and mean + assert abs(result_sum.item() - 18.7097) < 1e-3 + assert abs(result_mean.item() - 0.0244) < 1e-3 + + def test_full_loop_multistep(self): + sample = self.full_loop(num_inference_steps=10) + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + # TODO: get expected sum and mean + assert abs(result_sum.item() - 197.7616) < 1e-3 + assert abs(result_mean.item() - 0.2575) < 1e-3 + + def test_custom_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 1, 0] + + scheduler.set_timesteps(timesteps=timesteps) + + scheduler_timesteps = scheduler.timesteps + + for i, timestep in enumerate(scheduler_timesteps): + if i == len(timesteps) - 1: + expected_prev_t = -1 + else: + expected_prev_t = timesteps[i + 1] + + prev_t = scheduler.previous_timestep(timestep) + prev_t = prev_t.item() + + self.assertEqual(prev_t, expected_prev_t) + + def test_custom_timesteps_increasing_order(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 51, 0] + + with self.assertRaises(ValueError, msg="`custom_timesteps` must be in descending order."): + scheduler.set_timesteps(timesteps=timesteps) + + def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 1, 0] + num_inference_steps = len(timesteps) + + with self.assertRaises(ValueError, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."): + scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps) + + def test_custom_timesteps_too_large(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [scheduler.config.num_train_timesteps] + + with self.assertRaises( + ValueError, + msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}", + ): + scheduler.set_timesteps(timesteps=timesteps) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_lms.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_lms.py new file mode 100644 index 0000000000000000000000000000000000000000..c4abca3ac973dc00870efd564e66bcdf53c2bd6e --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_lms.py @@ -0,0 +1,176 @@ +import torch + +from diffusers import LMSDiscreteScheduler + +from ..testing_utils import torch_device +from .test_schedulers import SchedulerCommonTest + + +class LMSDiscreteSchedulerTest(SchedulerCommonTest): + scheduler_classes = (LMSDiscreteScheduler,) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1100, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_time_indices(self): + for t in [0, 500, 800]: + self.check_over_forward(time_step=t) + + def test_full_loop_no_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 1006.388) < 1e-2 + assert abs(result_mean.item() - 1.31) < 1e-3 + + def test_full_loop_with_v_prediction(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 0.0017) < 1e-2 + assert abs(result_mean.item() - 2.2676e-06) < 1e-3 + + def test_full_loop_device(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() + sample = sample.to(torch_device) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 1006.388) < 1e-2 + assert abs(result_mean.item() - 1.31) < 1e-3 + + def test_full_loop_device_karras_sigmas(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + sample = sample.to(torch_device) + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 3812.9927) < 2e-2 + assert abs(result_mean.item() - 4.9648) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + + # add noise + t_start = self.num_inference_steps - 2 + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 27663.6895) < 1e-2 + assert abs(result_mean.item() - 36.0204) < 1e-3 + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_pndm.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_pndm.py new file mode 100644 index 0000000000000000000000000000000000000000..13c6904682223de2382b2d0d7b38e4a435f97988 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_pndm.py @@ -0,0 +1,244 @@ +import tempfile +import unittest + +import torch + +from diffusers import PNDMScheduler + +from .test_schedulers import SchedulerCommonTest + + +class PNDMSchedulerTest(SchedulerCommonTest): + scheduler_classes = (PNDMScheduler,) + forward_default_kwargs = (("num_inference_steps", 50),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.ets = dummy_past_residuals[:] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.ets = dummy_past_residuals[:] + + output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + @unittest.skip("Test not supported.") + def test_from_save_pretrained(self): + pass + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.ets = dummy_past_residuals[:] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.ets = dummy_past_residuals[:] + + output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + output = scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.prk_timesteps): + residual = model(sample, t) + sample = scheduler.step_prk(residual, t, sample).prev_sample + + for i, t in enumerate(scheduler.plms_timesteps): + residual = model(sample, t) + sample = scheduler.step_plms(residual, t, sample).prev_sample + + return sample + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] + scheduler.ets = dummy_past_residuals[:] + + output_0 = scheduler.step_prk(residual, 0, sample, **kwargs).prev_sample + output_1 = scheduler.step_prk(residual, 1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + output_0 = scheduler.step_plms(residual, 0, sample, **kwargs).prev_sample + output_1 = scheduler.step_plms(residual, 1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_timesteps(self): + for timesteps in [100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_steps_offset(self): + for steps_offset in [0, 1]: + self.check_over_configs(steps_offset=steps_offset) + + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(steps_offset=1) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(10) + assert torch.equal( + scheduler.timesteps, + torch.LongTensor( + [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] + ), + ) + + def test_betas(self): + for beta_start, beta_end in zip([0.0001, 0.001], [0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "squaredcos_cap_v2"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_time_indices(self): + for t in [1, 5, 10]: + self.check_over_forward(time_step=t) + + def test_inference_steps(self): + for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]): + self.check_over_forward(num_inference_steps=num_inference_steps) + + def test_pow_of_3_inference_steps(self): + # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3 + num_inference_steps = 27 + + for scheduler_class in self.scheduler_classes: + sample = self.dummy_sample + residual = 0.1 * sample + + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + + # before power of 3 fix, would error on first step, so we only need to do two + for i, t in enumerate(scheduler.prk_timesteps[:2]): + sample = scheduler.step_prk(residual, t, sample).prev_sample + + def test_inference_plms_no_past_residuals(self): + with self.assertRaises(ValueError): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.step_plms(self.dummy_sample, 1, self.dummy_sample).prev_sample + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 198.1318) < 1e-2 + assert abs(result_mean.item() - 0.2580) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 67.3986) < 1e-2 + assert abs(result_mean.item() - 0.0878) < 1e-3 + + def test_full_loop_with_set_alpha_to_one(self): + # We specify different beta, so that the first alpha is 0.99 + sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01) + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 230.0399) < 1e-2 + assert abs(result_mean.item() - 0.2995) < 1e-3 + + def test_full_loop_with_no_set_alpha_to_one(self): + # We specify different beta, so that the first alpha is 0.99 + sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01) + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 186.9482) < 1e-2 + assert abs(result_mean.item() - 0.2434) < 1e-3 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_sasolver.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_sasolver.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2d2c0397bb620bbf556ac8d4701eb17494e36b --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_sasolver.py @@ -0,0 +1,200 @@ +import torch + +from diffusers import SASolverScheduler + +from ..testing_utils import require_torchsde, torch_device +from .test_schedulers import SchedulerCommonTest + + +@require_torchsde +class SASolverSchedulerTest(SchedulerCommonTest): + scheduler_classes = (SASolverScheduler,) + forward_default_kwargs = (("num_inference_steps", 10),) + num_inference_steps = 10 + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1100, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + } + + config.update(**kwargs) + return config + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + scheduler.model_outputs = dummy_past_residuals[ + : max( + scheduler.config.predictor_order, + scheduler.config.corrector_order - 1, + ) + ] + + time_step_0 = scheduler.timesteps[5] + time_step_1 = scheduler.timesteps[6] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_timesteps(self): + for timesteps in [10, 50, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]): + self.check_over_configs(beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear"]: + self.check_over_configs(beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_full_loop_no_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + generator = torch.manual_seed(0) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t, generator=generator) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["cpu"]: + assert abs(result_sum.item() - 337.394287109375) < 1e-2 + assert abs(result_mean.item() - 0.43931546807289124) < 1e-3 + elif torch_device in ["cuda"]: + assert abs(result_sum.item() - 329.1999816894531) < 1e-2 + assert abs(result_mean.item() - 0.4286458194255829) < 1e-3 + + def test_full_loop_with_v_prediction(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(prediction_type="v_prediction") + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps) + + model = self.dummy_model() + sample = self.dummy_sample_deter * scheduler.init_noise_sigma + sample = sample.to(torch_device) + generator = torch.manual_seed(0) + + for i, t in enumerate(scheduler.timesteps): + sample = scheduler.scale_model_input(sample, t, generator=generator) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["cpu"]: + assert abs(result_sum.item() - 193.1467742919922) < 1e-2 + assert abs(result_mean.item() - 0.2514931857585907) < 1e-3 + elif torch_device in ["cuda"]: + assert abs(result_sum.item() - 193.4154052734375) < 1e-2 + assert abs(result_mean.item() - 0.2518429756164551) < 1e-3 + + def test_full_loop_device(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + generator = torch.manual_seed(0) + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["cpu"]: + assert abs(result_sum.item() - 337.394287109375) < 1e-2 + assert abs(result_mean.item() - 0.43931546807289124) < 1e-3 + elif torch_device in ["cuda"]: + assert abs(result_sum.item() - 337.394287109375) < 1e-2 + assert abs(result_mean.item() - 0.4393154978752136) < 1e-3 + + def test_full_loop_device_karras_sigmas(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True) + + scheduler.set_timesteps(self.num_inference_steps, device=torch_device) + + model = self.dummy_model() + sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma + sample = sample.to(torch_device) + generator = torch.manual_seed(0) + + for t in scheduler.timesteps: + sample = scheduler.scale_model_input(sample, t) + + model_output = model(sample, t) + + output = scheduler.step(model_output, t, sample, generator=generator) + sample = output.prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + if torch_device in ["cpu"]: + assert abs(result_sum.item() - 837.2554931640625) < 1e-2 + assert abs(result_mean.item() - 1.0901764631271362) < 1e-2 + elif torch_device in ["cuda"]: + assert abs(result_sum.item() - 837.25537109375) < 1e-2 + assert abs(result_mean.item() - 1.0901763439178467) < 1e-2 + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_score_sde_ve.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_score_sde_ve.py new file mode 100644 index 0000000000000000000000000000000000000000..08c30f9b1e0c2ce1f7baab82f5076efabe465a69 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_score_sde_ve.py @@ -0,0 +1,189 @@ +import tempfile +import unittest + +import numpy as np +import torch + +from diffusers import ScoreSdeVeScheduler + + +class ScoreSdeVeSchedulerTest(unittest.TestCase): + # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration) + scheduler_classes = (ScoreSdeVeScheduler,) + forward_default_kwargs = () + + @property + def dummy_sample(self): + batch_size = 4 + num_channels = 3 + height = 8 + width = 8 + + sample = torch.rand((batch_size, num_channels, height, width)) + + return sample + + @property + def dummy_sample_deter(self): + batch_size = 4 + num_channels = 3 + height = 8 + width = 8 + + num_elems = batch_size * num_channels * height * width + sample = torch.arange(num_elems) + sample = sample.reshape(num_channels, height, width, batch_size) + sample = sample / num_elems + sample = sample.permute(3, 0, 1, 2) + + return sample + + def dummy_model(self): + def model(sample, t, *args): + return sample * t / (t + 1) + + return model + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 2000, + "snr": 0.15, + "sigma_min": 0.01, + "sigma_max": 1348, + "sampling_eps": 1e-5, + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + + for scheduler_class in self.scheduler_classes: + sample = self.dummy_sample + residual = 0.1 * sample + + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + output = scheduler.step_pred( + residual, time_step, sample, generator=torch.manual_seed(0), **kwargs + ).prev_sample + new_output = new_scheduler.step_pred( + residual, time_step, sample, generator=torch.manual_seed(0), **kwargs + ).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + output = scheduler.step_correct(residual, sample, generator=torch.manual_seed(0), **kwargs).prev_sample + new_output = new_scheduler.step_correct( + residual, sample, generator=torch.manual_seed(0), **kwargs + ).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical" + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + kwargs.update(forward_kwargs) + + for scheduler_class in self.scheduler_classes: + sample = self.dummy_sample + residual = 0.1 * sample + + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + output = scheduler.step_pred( + residual, time_step, sample, generator=torch.manual_seed(0), **kwargs + ).prev_sample + new_output = new_scheduler.step_pred( + residual, time_step, sample, generator=torch.manual_seed(0), **kwargs + ).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + output = scheduler.step_correct(residual, sample, generator=torch.manual_seed(0), **kwargs).prev_sample + new_output = new_scheduler.step_correct( + residual, sample, generator=torch.manual_seed(0), **kwargs + ).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical" + + def test_timesteps(self): + for timesteps in [10, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_sigmas(self): + for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01], [1, 100, 1000]): + self.check_over_configs(sigma_min=sigma_min, sigma_max=sigma_max) + + def test_time_indices(self): + for t in [0.1, 0.5, 0.75]: + self.check_over_forward(time_step=t) + + def test_full_loop_no_noise(self): + kwargs = dict(self.forward_default_kwargs) + + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 3 + + model = self.dummy_model() + sample = self.dummy_sample_deter + + scheduler.set_sigmas(num_inference_steps) + scheduler.set_timesteps(num_inference_steps) + generator = torch.manual_seed(0) + + for i, t in enumerate(scheduler.timesteps): + sigma_t = scheduler.sigmas[i] + + for _ in range(scheduler.config.correct_steps): + with torch.no_grad(): + model_output = model(sample, sigma_t) + sample = scheduler.step_correct(model_output, sample, generator=generator, **kwargs).prev_sample + + with torch.no_grad(): + model_output = model(sample, sigma_t) + + output = scheduler.step_pred(model_output, t, sample, generator=generator, **kwargs) + sample, _ = output.prev_sample, output.prev_sample_mean + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert np.isclose(result_sum.item(), 14372758528.0) + assert np.isclose(result_mean.item(), 18714530.0) + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + output_0 = scheduler.step_pred(residual, 0, sample, generator=torch.manual_seed(0), **kwargs).prev_sample + output_1 = scheduler.step_pred(residual, 1, sample, generator=torch.manual_seed(0), **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_tcd.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_tcd.py new file mode 100644 index 0000000000000000000000000000000000000000..e95c536c7f0a279df379d872a335c1e6b30d6ccc --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_tcd.py @@ -0,0 +1,180 @@ +import torch + +from diffusers import TCDScheduler + +from .test_schedulers import SchedulerCommonTest + + +class TCDSchedulerTest(SchedulerCommonTest): + scheduler_classes = (TCDScheduler,) + forward_default_kwargs = (("num_inference_steps", 10),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.00085, + "beta_end": 0.0120, + "beta_schedule": "scaled_linear", + "prediction_type": "epsilon", + } + + config.update(**kwargs) + return config + + @property + def default_num_inference_steps(self): + return 10 + + @property + def default_valid_timestep(self): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + + scheduler_config = self.get_scheduler_config() + scheduler = self.scheduler_classes[0](**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + timestep = scheduler.timesteps[-1] + return timestep + + def test_timesteps(self): + for timesteps in [100, 500, 1000]: + # 0 is not guaranteed to be in the timestep schedule, but timesteps - 1 is + self.check_over_configs(time_step=timesteps - 1, num_train_timesteps=timesteps) + + def test_betas(self): + for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]): + self.check_over_configs(time_step=self.default_valid_timestep, beta_start=beta_start, beta_end=beta_end) + + def test_schedules(self): + for schedule in ["linear", "scaled_linear", "squaredcos_cap_v2"]: + self.check_over_configs(time_step=self.default_valid_timestep, beta_schedule=schedule) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(time_step=self.default_valid_timestep, prediction_type=prediction_type) + + def test_clip_sample(self): + for clip_sample in [True, False]: + self.check_over_configs(time_step=self.default_valid_timestep, clip_sample=clip_sample) + + def test_thresholding(self): + self.check_over_configs(time_step=self.default_valid_timestep, thresholding=False) + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs( + time_step=self.default_valid_timestep, + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + ) + + def test_time_indices(self): + # Get default timestep schedule. + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + + scheduler_config = self.get_scheduler_config() + scheduler = self.scheduler_classes[0](**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + timesteps = scheduler.timesteps + for t in timesteps: + self.check_over_forward(time_step=t) + + def test_inference_steps(self): + # Hardcoded for now + for t, num_inference_steps in zip([99, 39, 39, 19], [10, 25, 26, 50]): + self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps) + + def full_loop(self, num_inference_steps=10, seed=0, **config): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + eta = 0.0 # refer to gamma in the paper + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(seed) + scheduler.set_timesteps(num_inference_steps) + + for t in scheduler.timesteps: + residual = model(sample, t) + sample = scheduler.step(residual, t, sample, eta, generator).prev_sample + + return sample + + def test_full_loop_onestep_deter(self): + sample = self.full_loop(num_inference_steps=1) + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 29.8715) < 1e-3 # 0.0778918 + assert abs(result_mean.item() - 0.0389) < 1e-3 + + def test_full_loop_multistep_deter(self): + sample = self.full_loop(num_inference_steps=10) + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 181.2040) < 1e-3 + assert abs(result_mean.item() - 0.2359) < 1e-3 + + def test_custom_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 1, 0] + + scheduler.set_timesteps(timesteps=timesteps) + + scheduler_timesteps = scheduler.timesteps + + for i, timestep in enumerate(scheduler_timesteps): + if i == len(timesteps) - 1: + expected_prev_t = -1 + else: + expected_prev_t = timesteps[i + 1] + + prev_t = scheduler.previous_timestep(timestep) + prev_t = prev_t.item() + + self.assertEqual(prev_t, expected_prev_t) + + def test_custom_timesteps_increasing_order(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 51, 0] + + with self.assertRaises(ValueError, msg="`custom_timesteps` must be in descending order."): + scheduler.set_timesteps(timesteps=timesteps) + + def test_custom_timesteps_passing_both_num_inference_steps_and_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [100, 87, 50, 1, 0] + num_inference_steps = len(timesteps) + + with self.assertRaises(ValueError, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."): + scheduler.set_timesteps(num_inference_steps=num_inference_steps, timesteps=timesteps) + + def test_custom_timesteps_too_large(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = [scheduler.config.num_train_timesteps] + + with self.assertRaises( + ValueError, + msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}", + ): + scheduler.set_timesteps(timesteps=timesteps) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_unclip.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_unclip.py new file mode 100644 index 0000000000000000000000000000000000000000..9e66a328f42e228a9ed02f6ca52d07f7e8944d64 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_unclip.py @@ -0,0 +1,141 @@ +import unittest + +import torch + +from diffusers import UnCLIPScheduler + +from .test_schedulers import SchedulerCommonTest + + +# UnCLIPScheduler is a modified DDPMScheduler with a subset of the configuration. +class UnCLIPSchedulerTest(SchedulerCommonTest): + scheduler_classes = (UnCLIPScheduler,) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "variance_type": "fixed_small_log", + "clip_sample": True, + "clip_sample_range": 1.0, + "prediction_type": "epsilon", + } + + config.update(**kwargs) + return config + + def test_timesteps(self): + for timesteps in [1, 5, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_variance_type(self): + for variance in ["fixed_small_log", "learned_range"]: + self.check_over_configs(variance_type=variance) + + def test_clip_sample(self): + for clip_sample in [True, False]: + self.check_over_configs(clip_sample=clip_sample) + + def test_clip_sample_range(self): + for clip_sample_range in [1, 5, 10, 20]: + self.check_over_configs(clip_sample_range=clip_sample_range) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_time_indices(self): + for time_step in [0, 500, 999]: + for prev_timestep in [None, 5, 100, 250, 500, 750]: + if prev_timestep is not None and prev_timestep >= time_step: + continue + + self.check_over_forward(time_step=time_step, prev_timestep=prev_timestep) + + def test_variance_fixed_small_log(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(variance_type="fixed_small_log") + scheduler = scheduler_class(**scheduler_config) + + assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1e-5 + assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1e-5 + + def test_variance_learned_range(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(variance_type="learned_range") + scheduler = scheduler_class(**scheduler_config) + + predicted_variance = 0.5 + + assert scheduler._get_variance(1, predicted_variance=predicted_variance) - -10.1712790 < 1e-5 + assert scheduler._get_variance(487, predicted_variance=predicted_variance) - -5.7998052 < 1e-5 + assert scheduler._get_variance(999, predicted_variance=predicted_variance) - -0.0010011 < 1e-5 + + def test_full_loop(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + timesteps = scheduler.timesteps + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(0) + + for i, t in enumerate(timesteps): + # 1. predict noise residual + residual = model(sample, t) + + # 2. predict previous mean of sample x_t-1 + pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample + + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 252.2682495) < 1e-2 + assert abs(result_mean.item() - 0.3284743) < 1e-3 + + def test_full_loop_skip_timesteps(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + scheduler.set_timesteps(25) + + timesteps = scheduler.timesteps + + model = self.dummy_model() + sample = self.dummy_sample_deter + generator = torch.manual_seed(0) + + for i, t in enumerate(timesteps): + # 1. predict noise residual + residual = model(sample, t) + + if i + 1 == timesteps.shape[0]: + prev_timestep = None + else: + prev_timestep = timesteps[i + 1] + + # 2. predict previous mean of sample x_t-1 + pred_prev_sample = scheduler.step( + residual, t, sample, prev_timestep=prev_timestep, generator=generator + ).prev_sample + + sample = pred_prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 258.2044983) < 1e-2 + assert abs(result_mean.item() - 0.3362038) < 1e-3 + + @unittest.skip("Test not supported.") + def test_trained_betas(self): + pass + + @unittest.skip("Test not supported.") + def test_add_noise_device(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_unipc.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_unipc.py new file mode 100644 index 0000000000000000000000000000000000000000..ac7e1d3f88b4c4d4e265260e1753023332acde93 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_unipc.py @@ -0,0 +1,430 @@ +import tempfile + +import torch + +from diffusers import ( + DEISMultistepScheduler, + DPMSolverMultistepScheduler, + DPMSolverSinglestepScheduler, + UniPCMultistepScheduler, +) + +from .test_schedulers import SchedulerCommonTest + + +class UniPCMultistepSchedulerTest(SchedulerCommonTest): + scheduler_classes = (UniPCMultistepScheduler,) + forward_default_kwargs = (("num_inference_steps", 25),) + + def get_scheduler_config(self, **kwargs): + config = { + "num_train_timesteps": 1000, + "beta_start": 0.0001, + "beta_end": 0.02, + "beta_schedule": "linear", + "solver_order": 2, + "solver_type": "bh2", + "final_sigmas_type": "sigma_min", + } + + config.update(**kwargs) + return config + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + new_scheduler.set_timesteps(num_inference_steps) + # copy over dummy past residuals + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output, new_output = sample, sample + for t in range(time_step, time_step + scheduler.config.solver_order + 1): + t = scheduler.timesteps[t] + output = scheduler.step(residual, t, output, **kwargs).prev_sample + new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", None) + sample = self.dummy_sample + residual = 0.1 * sample + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residuals (must be after setting timesteps) + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + # copy over dummy past residuals + new_scheduler.set_timesteps(num_inference_steps) + + # copy over dummy past residual (must be after setting timesteps) + new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order] + + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def full_loop(self, scheduler=None, **config): + if scheduler is None: + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + return sample + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # copy over dummy past residuals (must be done after set_timesteps) + dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10] + scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order] + + time_step_0 = scheduler.timesteps[5] + time_step_1 = scheduler.timesteps[6] + + output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_switch(self): + # make sure that iterating over schedulers with same config names gives same results + # for defaults + scheduler = UniPCMultistepScheduler(**self.get_scheduler_config()) + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2464) < 1e-3 + + scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config) + scheduler = DEISMultistepScheduler.from_config(scheduler.config) + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + scheduler = UniPCMultistepScheduler.from_config(scheduler.config) + + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2464) < 1e-3 + + def test_timesteps(self): + for timesteps in [25, 50, 100, 999, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_thresholding(self): + self.check_over_configs(thresholding=False) + for order in [1, 2, 3]: + for solver_type in ["bh1", "bh2"]: + for threshold in [0.5, 1.0, 2.0]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + thresholding=True, + prediction_type=prediction_type, + sample_max_value=threshold, + solver_order=order, + solver_type=solver_type, + ) + + def test_prediction_type(self): + for prediction_type in ["epsilon", "v_prediction"]: + self.check_over_configs(prediction_type=prediction_type) + + def test_rescale_betas_zero_snr(self): + for rescale_betas_zero_snr in [True, False]: + self.check_over_configs(rescale_betas_zero_snr=rescale_betas_zero_snr) + + def test_solver_order_and_type(self): + for solver_type in ["bh1", "bh2"]: + for order in [1, 2, 3]: + for prediction_type in ["epsilon", "sample"]: + self.check_over_configs( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + ) + sample = self.full_loop( + solver_order=order, + solver_type=solver_type, + prediction_type=prediction_type, + ) + assert not torch.isnan(sample).any(), "Samples have nan numbers" + + def test_lower_order_final(self): + self.check_over_configs(lower_order_final=True) + self.check_over_configs(lower_order_final=False) + + def test_inference_steps(self): + for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]: + self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0) + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2464) < 1e-3 + + def test_full_loop_with_karras(self): + sample = self.full_loop(use_karras_sigmas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2925) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.1014) < 1e-3 + + def test_full_loop_with_karras_and_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction", use_karras_sigmas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.1966) < 1e-3 + + def test_fp16_support(self): + for order in [1, 2, 3]: + for solver_type in ["bh1", "bh2"]: + for prediction_type in ["epsilon", "sample", "v_prediction"]: + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config( + thresholding=True, + dynamic_thresholding_ratio=0, + prediction_type=prediction_type, + solver_order=order, + solver_type=solver_type, + ) + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + model = self.dummy_model() + sample = self.dummy_sample_deter.half() + scheduler.set_timesteps(num_inference_steps) + + for i, t in enumerate(scheduler.timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + assert sample.dtype == torch.float16 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + t_start = 8 + + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 315.5757) < 1e-2, f" expected result sum 315.5757, but get {result_sum}" + assert abs(result_mean.item() - 0.4109) < 1e-3, f" expected result mean 0.4109, but get {result_mean}" + + +class UniPCMultistepScheduler1DTest(UniPCMultistepSchedulerTest): + @property + def dummy_sample(self): + batch_size = 4 + num_channels = 3 + width = 8 + + sample = torch.rand((batch_size, num_channels, width)) + + return sample + + @property + def dummy_noise_deter(self): + batch_size = 4 + num_channels = 3 + width = 8 + + num_elems = batch_size * num_channels * width + sample = torch.arange(num_elems).flip(-1) + sample = sample.reshape(num_channels, width, batch_size) + sample = sample / num_elems + sample = sample.permute(2, 0, 1) + + return sample + + @property + def dummy_sample_deter(self): + batch_size = 4 + num_channels = 3 + width = 8 + + num_elems = batch_size * num_channels * width + sample = torch.arange(num_elems) + sample = sample.reshape(num_channels, width, batch_size) + sample = sample / num_elems + sample = sample.permute(2, 0, 1) + + return sample + + def test_switch(self): + # make sure that iterating over schedulers with same config names gives same results + # for defaults + scheduler = UniPCMultistepScheduler(**self.get_scheduler_config()) + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2441) < 1e-3 + + scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config) + scheduler = DEISMultistepScheduler.from_config(scheduler.config) + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + scheduler = UniPCMultistepScheduler.from_config(scheduler.config) + + sample = self.full_loop(scheduler=scheduler) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2441) < 1e-3 + + def test_full_loop_no_noise(self): + sample = self.full_loop() + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2441) < 1e-3 + + def test_full_loop_with_karras(self): + sample = self.full_loop(use_karras_sigmas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.2898) < 1e-3 + + def test_full_loop_with_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction") + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.1014) < 1e-3 + + def test_full_loop_with_karras_and_v_prediction(self): + sample = self.full_loop(prediction_type="v_prediction", use_karras_sigmas=True) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_mean.item() - 0.1944) < 1e-3 + + def test_full_loop_with_noise(self): + scheduler_class = self.scheduler_classes[0] + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + num_inference_steps = 10 + t_start = 8 + + model = self.dummy_model() + sample = self.dummy_sample_deter + scheduler.set_timesteps(num_inference_steps) + + # add noise + noise = self.dummy_noise_deter + timesteps = scheduler.timesteps[t_start * scheduler.order :] + sample = scheduler.add_noise(sample, noise, timesteps[:1]) + + for i, t in enumerate(timesteps): + residual = model(sample, t) + sample = scheduler.step(residual, t, sample).prev_sample + + result_sum = torch.sum(torch.abs(sample)) + result_mean = torch.mean(torch.abs(sample)) + + assert abs(result_sum.item() - 39.0870) < 1e-2, f" expected result sum 39.0870, but get {result_sum}" + assert abs(result_mean.item() - 0.4072) < 1e-3, f" expected result mean 0.4072, but get {result_mean}" + + def test_beta_sigmas(self): + self.check_over_configs(use_beta_sigmas=True) + + def test_exponential_sigmas(self): + self.check_over_configs(use_exponential_sigmas=True) + + def test_flow_and_karras_sigmas(self): + self.check_over_configs(use_flow_sigmas=True, use_karras_sigmas=True) + + def test_flow_and_karras_sigmas_values(self): + num_train_timesteps = 1000 + num_inference_steps = 5 + scheduler = UniPCMultistepScheduler( + sigma_min=0.01, + sigma_max=200.0, + use_flow_sigmas=True, + use_karras_sigmas=True, + num_train_timesteps=num_train_timesteps, + ) + scheduler.set_timesteps(num_inference_steps=num_inference_steps) + + expected_sigmas = [ + 0.9950248599052429, + 0.9787454605102539, + 0.8774884343147278, + 0.3604971766471863, + 0.009900986216962337, + 0.0, # 0 appended as default + ] + expected_sigmas = torch.tensor(expected_sigmas) + expected_timesteps = (expected_sigmas * num_train_timesteps).to(torch.int64) + expected_timesteps = expected_timesteps[0:-1] + self.assertTrue(torch.allclose(scheduler.sigmas, expected_sigmas)) + self.assertTrue(torch.all(expected_timesteps == scheduler.timesteps)) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_vq_diffusion.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_vq_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..c12825ba2e620550910be6d50a273f7043edc7fe --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_scheduler_vq_diffusion.py @@ -0,0 +1,59 @@ +import unittest + +import torch +import torch.nn.functional as F + +from diffusers import VQDiffusionScheduler + +from .test_schedulers import SchedulerCommonTest + + +class VQDiffusionSchedulerTest(SchedulerCommonTest): + scheduler_classes = (VQDiffusionScheduler,) + + def get_scheduler_config(self, **kwargs): + config = { + "num_vec_classes": 4097, + "num_train_timesteps": 100, + } + + config.update(**kwargs) + return config + + def dummy_sample(self, num_vec_classes): + batch_size = 4 + height = 8 + width = 8 + + sample = torch.randint(0, num_vec_classes, (batch_size, height * width)) + + return sample + + @property + def dummy_sample_deter(self): + assert False + + def dummy_model(self, num_vec_classes): + def model(sample, t, *args): + batch_size, num_latent_pixels = sample.shape + logits = torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)) + return_value = F.log_softmax(logits.double(), dim=1).float() + return return_value + + return model + + def test_timesteps(self): + for timesteps in [2, 5, 100, 1000]: + self.check_over_configs(num_train_timesteps=timesteps) + + def test_num_vec_classes(self): + for num_vec_classes in [5, 100, 1000, 4000]: + self.check_over_configs(num_vec_classes=num_vec_classes) + + def test_time_indices(self): + for t in [0, 50, 99]: + self.check_over_forward(time_step=t) + + @unittest.skip("Test not supported.") + def test_add_noise_device(self): + pass diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_schedulers.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_schedulers.py new file mode 100644 index 0000000000000000000000000000000000000000..5a8380e659fcebcf35283ef0861c0b95e6185271 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/schedulers/test_schedulers.py @@ -0,0 +1,868 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import inspect +import json +import os +import tempfile +import unittest +import uuid +from typing import Dict, List, Tuple + +import numpy as np +import torch +from huggingface_hub import delete_repo + +import diffusers +from diffusers import ( + CMStochasticIterativeScheduler, + DDIMScheduler, + DEISMultistepScheduler, + DiffusionPipeline, + EDMEulerScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + IPNDMScheduler, + LMSDiscreteScheduler, + UniPCMultistepScheduler, + VQDiffusionScheduler, +) +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.schedulers.scheduling_utils import SchedulerMixin +from diffusers.utils import logging + +from ..others.test_utils import TOKEN, USER, is_staging_test +from ..testing_utils import CaptureLogger, torch_device + + +torch.backends.cuda.matmul.allow_tf32 = False + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class SchedulerObject(SchedulerMixin, ConfigMixin): + config_name = "config.json" + + @register_to_config + def __init__( + self, + a=2, + b=5, + c=(2, 5), + d="for diffusion", + e=[1, 3], + ): + pass + + +class SchedulerObject2(SchedulerMixin, ConfigMixin): + config_name = "config.json" + + @register_to_config + def __init__( + self, + a=2, + b=5, + c=(2, 5), + d="for diffusion", + f=[1, 3], + ): + pass + + +class SchedulerObject3(SchedulerMixin, ConfigMixin): + config_name = "config.json" + + @register_to_config + def __init__( + self, + a=2, + b=5, + c=(2, 5), + d="for diffusion", + e=[1, 3], + f=[1, 3], + ): + pass + + +class SchedulerBaseTests(unittest.TestCase): + def test_save_load_from_different_config(self): + obj = SchedulerObject() + + # mock add obj class to `diffusers` + setattr(diffusers, "SchedulerObject", SchedulerObject) + logger = logging.get_logger("diffusers.configuration_utils") + + with tempfile.TemporaryDirectory() as tmpdirname: + obj.save_config(tmpdirname) + with CaptureLogger(logger) as cap_logger_1: + config = SchedulerObject2.load_config(tmpdirname) + new_obj_1 = SchedulerObject2.from_config(config) + + # now save a config parameter that is not expected + with open(os.path.join(tmpdirname, SchedulerObject.config_name), "r") as f: + data = json.load(f) + data["unexpected"] = True + + with open(os.path.join(tmpdirname, SchedulerObject.config_name), "w") as f: + json.dump(data, f) + + with CaptureLogger(logger) as cap_logger_2: + config = SchedulerObject.load_config(tmpdirname) + new_obj_2 = SchedulerObject.from_config(config) + + with CaptureLogger(logger) as cap_logger_3: + config = SchedulerObject2.load_config(tmpdirname) + new_obj_3 = SchedulerObject2.from_config(config) + + assert new_obj_1.__class__ == SchedulerObject2 + assert new_obj_2.__class__ == SchedulerObject + assert new_obj_3.__class__ == SchedulerObject2 + + assert cap_logger_1.out == "" + assert ( + cap_logger_2.out + == "The config attributes {'unexpected': True} were passed to SchedulerObject, but are not expected and" + " will" + " be ignored. Please verify your config.json configuration file.\n" + ) + assert cap_logger_2.out.replace("SchedulerObject", "SchedulerObject2") == cap_logger_3.out + + def test_save_load_compatible_schedulers(self): + SchedulerObject2._compatibles = ["SchedulerObject"] + SchedulerObject._compatibles = ["SchedulerObject2"] + + obj = SchedulerObject() + + # mock add obj class to `diffusers` + setattr(diffusers, "SchedulerObject", SchedulerObject) + setattr(diffusers, "SchedulerObject2", SchedulerObject2) + logger = logging.get_logger("diffusers.configuration_utils") + + with tempfile.TemporaryDirectory() as tmpdirname: + obj.save_config(tmpdirname) + + # now save a config parameter that is expected by another class, but not origin class + with open(os.path.join(tmpdirname, SchedulerObject.config_name), "r") as f: + data = json.load(f) + data["f"] = [0, 0] + data["unexpected"] = True + + with open(os.path.join(tmpdirname, SchedulerObject.config_name), "w") as f: + json.dump(data, f) + + with CaptureLogger(logger) as cap_logger: + config = SchedulerObject.load_config(tmpdirname) + new_obj = SchedulerObject.from_config(config) + + assert new_obj.__class__ == SchedulerObject + + assert ( + cap_logger.out + == "The config attributes {'unexpected': True} were passed to SchedulerObject, but are not expected and" + " will" + " be ignored. Please verify your config.json configuration file.\n" + ) + + def test_save_load_from_different_config_comp_schedulers(self): + SchedulerObject3._compatibles = ["SchedulerObject", "SchedulerObject2"] + SchedulerObject2._compatibles = ["SchedulerObject", "SchedulerObject3"] + SchedulerObject._compatibles = ["SchedulerObject2", "SchedulerObject3"] + + obj = SchedulerObject() + + # mock add obj class to `diffusers` + setattr(diffusers, "SchedulerObject", SchedulerObject) + setattr(diffusers, "SchedulerObject2", SchedulerObject2) + setattr(diffusers, "SchedulerObject3", SchedulerObject3) + logger = logging.get_logger("diffusers.configuration_utils") + logger.setLevel(diffusers.logging.INFO) + + with tempfile.TemporaryDirectory() as tmpdirname: + obj.save_config(tmpdirname) + + with CaptureLogger(logger) as cap_logger_1: + config = SchedulerObject.load_config(tmpdirname) + new_obj_1 = SchedulerObject.from_config(config) + + with CaptureLogger(logger) as cap_logger_2: + config = SchedulerObject2.load_config(tmpdirname) + new_obj_2 = SchedulerObject2.from_config(config) + + with CaptureLogger(logger) as cap_logger_3: + config = SchedulerObject3.load_config(tmpdirname) + new_obj_3 = SchedulerObject3.from_config(config) + + assert new_obj_1.__class__ == SchedulerObject + assert new_obj_2.__class__ == SchedulerObject2 + assert new_obj_3.__class__ == SchedulerObject3 + + assert cap_logger_1.out == "" + assert cap_logger_2.out == "{'f'} was not found in config. Values will be initialized to default values.\n" + assert cap_logger_3.out == "{'f'} was not found in config. Values will be initialized to default values.\n" + + def test_default_arguments_not_in_config(self): + pipe = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-pipe", torch_dtype=torch.float16 + ) + assert pipe.scheduler.__class__ == DDIMScheduler + + # Default for DDIMScheduler + assert pipe.scheduler.config.timestep_spacing == "leading" + + # Switch to a different one, verify we use the default for that class + pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) + assert pipe.scheduler.config.timestep_spacing == "linspace" + + # Override with kwargs + pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") + assert pipe.scheduler.config.timestep_spacing == "trailing" + + # Verify overridden kwargs stick + pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) + assert pipe.scheduler.config.timestep_spacing == "trailing" + + # And stick + pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) + assert pipe.scheduler.config.timestep_spacing == "trailing" + + def test_default_solver_type_after_switch(self): + pipe = DiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-pipe", torch_dtype=torch.float16 + ) + assert pipe.scheduler.__class__ == DDIMScheduler + + pipe.scheduler = DEISMultistepScheduler.from_config(pipe.scheduler.config) + assert pipe.scheduler.config.solver_type == "logrho" + + # Switch to UniPC, verify the solver is the default + pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) + assert pipe.scheduler.config.solver_type == "bh2" + + +class SchedulerCommonTest(unittest.TestCase): + scheduler_classes = () + forward_default_kwargs = () + + @property + def default_num_inference_steps(self): + return 50 + + @property + def default_timestep(self): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.get("num_inference_steps", self.default_num_inference_steps) + + try: + scheduler_config = self.get_scheduler_config() + scheduler = self.scheduler_classes[0](**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + timestep = scheduler.timesteps[0] + except NotImplementedError: + logger.warning( + f"The scheduler {self.__class__.__name__} does not implement a `get_scheduler_config` method." + f" `default_timestep` will be set to the default value of 1." + ) + timestep = 1 + + return timestep + + # NOTE: currently taking the convention that default_timestep > default_timestep_2 (alternatively, + # default_timestep comes earlier in the timestep schedule than default_timestep_2) + @property + def default_timestep_2(self): + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.get("num_inference_steps", self.default_num_inference_steps) + + try: + scheduler_config = self.get_scheduler_config() + scheduler = self.scheduler_classes[0](**scheduler_config) + + scheduler.set_timesteps(num_inference_steps) + if len(scheduler.timesteps) >= 2: + timestep_2 = scheduler.timesteps[1] + else: + logger.warning( + f"Using num_inference_steps from the scheduler testing class's default config leads to a timestep" + f" scheduler of length {len(scheduler.timesteps)} < 2. The default `default_timestep_2` value of 0" + f" will be used." + ) + timestep_2 = 0 + except NotImplementedError: + logger.warning( + f"The scheduler {self.__class__.__name__} does not implement a `get_scheduler_config` method." + f" `default_timestep_2` will be set to the default value of 0." + ) + timestep_2 = 0 + + return timestep_2 + + @property + def dummy_sample(self): + batch_size = 4 + num_channels = 3 + height = 8 + width = 8 + + sample = torch.rand((batch_size, num_channels, height, width)) + + return sample + + @property + def dummy_noise_deter(self): + batch_size = 4 + num_channels = 3 + height = 8 + width = 8 + + num_elems = batch_size * num_channels * height * width + sample = torch.arange(num_elems).flip(-1) + sample = sample.reshape(num_channels, height, width, batch_size) + sample = sample / num_elems + sample = sample.permute(3, 0, 1, 2) + + return sample + + @property + def dummy_sample_deter(self): + batch_size = 4 + num_channels = 3 + height = 8 + width = 8 + + num_elems = batch_size * num_channels * height * width + sample = torch.arange(num_elems) + sample = sample.reshape(num_channels, height, width, batch_size) + sample = sample / num_elems + sample = sample.permute(3, 0, 1, 2) + + return sample + + def get_scheduler_config(self): + raise NotImplementedError + + def dummy_model(self): + def model(sample, t, *args): + # if t is a tensor, match the number of dimensions of sample + if isinstance(t, torch.Tensor): + num_dims = len(sample.shape) + # pad t with 1s to match num_dims + t = t.reshape(-1, *(1,) * (num_dims - 1)).to(sample.device, dtype=sample.dtype) + + return sample * t / (t + 1) + + return model + + def check_over_configs(self, time_step=0, **config): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + time_step = time_step if time_step is not None else self.default_timestep + + for scheduler_class in self.scheduler_classes: + # TODO(Suraj) - delete the following two lines once DDPM, DDIM, and PNDM have timesteps casted to float by default + if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler): + time_step = float(time_step) + + scheduler_config = self.get_scheduler_config(**config) + scheduler = scheduler_class(**scheduler_config) + + if scheduler_class == CMStochasticIterativeScheduler: + # Get valid timestep based on sigma_max, which should always be in timestep schedule. + scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max) + time_step = scaled_sigma_max + + if scheduler_class == EDMEulerScheduler: + time_step = scheduler.timesteps[-1] + + if scheduler_class == VQDiffusionScheduler: + num_vec_classes = scheduler_config["num_vec_classes"] + sample = self.dummy_sample(num_vec_classes) + model = self.dummy_model(num_vec_classes) + residual = model(sample, time_step) + else: + sample = self.dummy_sample + residual = 0.1 * sample + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + new_scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # Make sure `scale_model_input` is invoked to prevent a warning + if scheduler_class == CMStochasticIterativeScheduler: + # Get valid timestep based on sigma_max, which should always be in timestep schedule. + _ = scheduler.scale_model_input(sample, scaled_sigma_max) + _ = new_scheduler.scale_model_input(sample, scaled_sigma_max) + elif scheduler_class != VQDiffusionScheduler: + _ = scheduler.scale_model_input(sample, scheduler.timesteps[-1]) + _ = new_scheduler.scale_model_input(sample, scheduler.timesteps[-1]) + + # Set the seed before step() as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def check_over_forward(self, time_step=0, **forward_kwargs): + kwargs = dict(self.forward_default_kwargs) + kwargs.update(forward_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", None) + time_step = time_step if time_step is not None else self.default_timestep + + for scheduler_class in self.scheduler_classes: + if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler): + time_step = float(time_step) + + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + if scheduler_class == VQDiffusionScheduler: + num_vec_classes = scheduler_config["num_vec_classes"] + sample = self.dummy_sample(num_vec_classes) + model = self.dummy_model(num_vec_classes) + residual = model(sample, time_step) + else: + sample = self.dummy_sample + residual = 0.1 * sample + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + new_scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def test_from_save_pretrained(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", self.default_num_inference_steps) + + for scheduler_class in self.scheduler_classes: + timestep = self.default_timestep + if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler): + timestep = float(timestep) + + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + if scheduler_class == CMStochasticIterativeScheduler: + # Get valid timestep based on sigma_max, which should always be in timestep schedule. + timestep = scheduler.sigma_to_t(scheduler.config.sigma_max) + + if scheduler_class == VQDiffusionScheduler: + num_vec_classes = scheduler_config["num_vec_classes"] + sample = self.dummy_sample(num_vec_classes) + model = self.dummy_model(num_vec_classes) + residual = model(sample, timestep) + else: + sample = self.dummy_sample + residual = 0.1 * sample + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_config(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + new_scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample + + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + new_output = new_scheduler.step(residual, timestep, sample, **kwargs).prev_sample + + assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical" + + def test_compatibles(self): + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + + scheduler = scheduler_class(**scheduler_config) + + assert all(c is not None for c in scheduler.compatibles) + + for comp_scheduler_cls in scheduler.compatibles: + comp_scheduler = comp_scheduler_cls.from_config(scheduler.config) + assert comp_scheduler is not None + + new_scheduler = scheduler_class.from_config(comp_scheduler.config) + + new_scheduler_config = {k: v for k, v in new_scheduler.config.items() if k in scheduler.config} + scheduler_diff = {k: v for k, v in new_scheduler.config.items() if k not in scheduler.config} + + # make sure that configs are essentially identical + assert new_scheduler_config == dict(scheduler.config) + + # make sure that only differences are for configs that are not in init + init_keys = inspect.signature(scheduler_class.__init__).parameters.keys() + assert set(scheduler_diff.keys()).intersection(set(init_keys)) == set() + + def test_from_pretrained(self): + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + + scheduler = scheduler_class(**scheduler_config) + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_pretrained(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + # `_use_default_values` should not exist for just saved & loaded scheduler + scheduler_config = dict(scheduler.config) + del scheduler_config["_use_default_values"] + + assert scheduler_config == new_scheduler.config + + def test_step_shape(self): + kwargs = dict(self.forward_default_kwargs) + + num_inference_steps = kwargs.pop("num_inference_steps", self.default_num_inference_steps) + + timestep_0 = self.default_timestep + timestep_1 = self.default_timestep_2 + + for scheduler_class in self.scheduler_classes: + if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler): + timestep_0 = float(timestep_0) + timestep_1 = float(timestep_1) + + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + if scheduler_class == VQDiffusionScheduler: + num_vec_classes = scheduler_config["num_vec_classes"] + sample = self.dummy_sample(num_vec_classes) + model = self.dummy_model(num_vec_classes) + residual = model(sample, timestep_0) + else: + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + output_0 = scheduler.step(residual, timestep_0, sample, **kwargs).prev_sample + output_1 = scheduler.step(residual, timestep_1, sample, **kwargs).prev_sample + + self.assertEqual(output_0.shape, sample.shape) + self.assertEqual(output_0.shape, output_1.shape) + + def test_scheduler_outputs_equivalence(self): + def set_nan_tensor_to_zero(t): + t[t != t] = 0 + return t + + def recursive_check(tuple_object, dict_object): + if isinstance(tuple_object, (List, Tuple)): + for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()): + recursive_check(tuple_iterable_value, dict_iterable_value) + elif isinstance(tuple_object, Dict): + for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()): + recursive_check(tuple_iterable_value, dict_iterable_value) + elif tuple_object is None: + return + else: + self.assertTrue( + torch.allclose( + set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 + ), + msg=( + "Tuple and dict output are not equal. Difference:" + f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" + f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" + f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." + ), + ) + + kwargs = dict(self.forward_default_kwargs) + num_inference_steps = kwargs.pop("num_inference_steps", self.default_num_inference_steps) + + timestep = self.default_timestep + if len(self.scheduler_classes) > 0 and self.scheduler_classes[0] == IPNDMScheduler: + timestep = 1 + + for scheduler_class in self.scheduler_classes: + if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler): + timestep = float(timestep) + + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + if scheduler_class == CMStochasticIterativeScheduler: + # Get valid timestep based on sigma_max, which should always be in timestep schedule. + timestep = scheduler.sigma_to_t(scheduler.config.sigma_max) + + if scheduler_class == VQDiffusionScheduler: + num_vec_classes = scheduler_config["num_vec_classes"] + sample = self.dummy_sample(num_vec_classes) + model = self.dummy_model(num_vec_classes) + residual = model(sample, timestep) + else: + sample = self.dummy_sample + residual = 0.1 * sample + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + outputs_dict = scheduler.step(residual, timestep, sample, **kwargs) + + if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"): + scheduler.set_timesteps(num_inference_steps) + elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"): + kwargs["num_inference_steps"] = num_inference_steps + + # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler + if "generator" in set(inspect.signature(scheduler.step).parameters.keys()): + kwargs["generator"] = torch.manual_seed(0) + outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs) + + recursive_check(outputs_tuple, outputs_dict) + + def test_scheduler_public_api(self): + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + if scheduler_class != VQDiffusionScheduler: + self.assertTrue( + hasattr(scheduler, "init_noise_sigma"), + f"{scheduler_class} does not implement a required attribute `init_noise_sigma`", + ) + self.assertTrue( + hasattr(scheduler, "scale_model_input"), + ( + f"{scheduler_class} does not implement a required class method `scale_model_input(sample," + " timestep)`" + ), + ) + self.assertTrue( + hasattr(scheduler, "step"), + f"{scheduler_class} does not implement a required class method `step(...)`", + ) + + if scheduler_class != VQDiffusionScheduler: + sample = self.dummy_sample + if scheduler_class == CMStochasticIterativeScheduler: + # Get valid timestep based on sigma_max, which should always be in timestep schedule. + scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max) + scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max) + elif scheduler_class == EDMEulerScheduler: + scaled_sample = scheduler.scale_model_input(sample, scheduler.timesteps[-1]) + else: + scaled_sample = scheduler.scale_model_input(sample, 0.0) + self.assertEqual(sample.shape, scaled_sample.shape) + + def test_add_noise_device(self): + for scheduler_class in self.scheduler_classes: + if scheduler_class == IPNDMScheduler: + continue + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + scheduler.set_timesteps(self.default_num_inference_steps) + + sample = self.dummy_sample.to(torch_device) + if scheduler_class == CMStochasticIterativeScheduler: + # Get valid timestep based on sigma_max, which should always be in timestep schedule. + scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max) + scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max) + elif scheduler_class == EDMEulerScheduler: + scaled_sample = scheduler.scale_model_input(sample, scheduler.timesteps[-1]) + else: + scaled_sample = scheduler.scale_model_input(sample, 0.0) + self.assertEqual(sample.shape, scaled_sample.shape) + + noise = torch.randn(scaled_sample.shape).to(torch_device) + t = scheduler.timesteps[5][None] + noised = scheduler.add_noise(scaled_sample, noise, t) + self.assertEqual(noised.shape, scaled_sample.shape) + + def test_deprecated_kwargs(self): + for scheduler_class in self.scheduler_classes: + has_kwarg_in_model_class = "kwargs" in inspect.signature(scheduler_class.__init__).parameters + has_deprecated_kwarg = len(scheduler_class._deprecated_kwargs) > 0 + + if has_kwarg_in_model_class and not has_deprecated_kwarg: + raise ValueError( + f"{scheduler_class} has `**kwargs` in its __init__ method but has not defined any deprecated" + " kwargs under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if" + " there are no deprecated arguments or add the deprecated argument with `_deprecated_kwargs =" + " []`" + ) + + if not has_kwarg_in_model_class and has_deprecated_kwarg: + raise ValueError( + f"{scheduler_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated" + " kwargs under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs`" + f" argument to {self.model_class}.__init__ if there are deprecated arguments or remove the" + " deprecated argument from `_deprecated_kwargs = []`" + ) + + def test_trained_betas(self): + for scheduler_class in self.scheduler_classes: + if scheduler_class in (VQDiffusionScheduler, CMStochasticIterativeScheduler): + continue + + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config, trained_betas=np.array([0.1, 0.3])) + + with tempfile.TemporaryDirectory() as tmpdirname: + scheduler.save_pretrained(tmpdirname) + new_scheduler = scheduler_class.from_pretrained(tmpdirname) + + assert scheduler.betas.tolist() == new_scheduler.betas.tolist() + + def test_getattr_is_correct(self): + for scheduler_class in self.scheduler_classes: + scheduler_config = self.get_scheduler_config() + scheduler = scheduler_class(**scheduler_config) + + # save some things to test + scheduler.dummy_attribute = 5 + scheduler.register_to_config(test_attribute=5) + + logger = logging.get_logger("diffusers.configuration_utils") + # 30 for warning + logger.setLevel(30) + with CaptureLogger(logger) as cap_logger: + assert hasattr(scheduler, "dummy_attribute") + assert getattr(scheduler, "dummy_attribute") == 5 + assert scheduler.dummy_attribute == 5 + + # no warning should be thrown + assert cap_logger.out == "" + + logger = logging.get_logger("diffusers.schedulers.scheduling_utils") + # 30 for warning + logger.setLevel(30) + with CaptureLogger(logger) as cap_logger: + assert hasattr(scheduler, "save_pretrained") + fn = scheduler.save_pretrained + fn_1 = getattr(scheduler, "save_pretrained") + + assert fn == fn_1 + # no warning should be thrown + assert cap_logger.out == "" + + # warning should be thrown + with self.assertWarns(FutureWarning): + assert scheduler.test_attribute == 5 + + with self.assertWarns(FutureWarning): + assert getattr(scheduler, "test_attribute") == 5 + + with self.assertRaises(AttributeError) as error: + scheduler.does_not_exist + + assert str(error.exception) == f"'{type(scheduler).__name__}' object has no attribute 'does_not_exist'" + + +@is_staging_test +class SchedulerPushToHubTester(unittest.TestCase): + identifier = uuid.uuid4() + repo_id = f"test-scheduler-{identifier}" + org_repo_id = f"valid_org/{repo_id}-org" + + def test_push_to_hub(self): + scheduler = DDIMScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + ) + scheduler.push_to_hub(self.repo_id, token=TOKEN) + scheduler_loaded = DDIMScheduler.from_pretrained(f"{USER}/{self.repo_id}") + + assert type(scheduler) == type(scheduler_loaded) + + # Reset repo + delete_repo(token=TOKEN, repo_id=self.repo_id) + + # Push to hub via save_config + with tempfile.TemporaryDirectory() as tmp_dir: + scheduler.save_config(tmp_dir, repo_id=self.repo_id, push_to_hub=True, token=TOKEN) + + scheduler_loaded = DDIMScheduler.from_pretrained(f"{USER}/{self.repo_id}") + + assert type(scheduler) == type(scheduler_loaded) + + # Reset repo + delete_repo(token=TOKEN, repo_id=self.repo_id) + + def test_push_to_hub_in_organization(self): + scheduler = DDIMScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + ) + scheduler.push_to_hub(self.org_repo_id, token=TOKEN) + scheduler_loaded = DDIMScheduler.from_pretrained(self.org_repo_id) + + assert type(scheduler) == type(scheduler_loaded) + + # Reset repo + delete_repo(token=TOKEN, repo_id=self.org_repo_id) + + # Push to hub via save_config + with tempfile.TemporaryDirectory() as tmp_dir: + scheduler.save_config(tmp_dir, repo_id=self.org_repo_id, push_to_hub=True, token=TOKEN) + + scheduler_loaded = DDIMScheduler.from_pretrained(self.org_repo_id) + + assert type(scheduler) == type(scheduler_loaded) + + # Reset repo + delete_repo(token=TOKEN, repo_id=self.org_repo_id) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/__init__.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/single_file_testing_utils.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/single_file_testing_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..52fd2f5bfc7fa58bacb08eaf54bebf97570a67fc --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/single_file_testing_utils.py @@ -0,0 +1,503 @@ +import gc +import tempfile +from io import BytesIO + +import requests +import torch +from huggingface_hub import hf_hub_download, snapshot_download + +from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name +from diffusers.models.attention_processor import AttnProcessor + +from ..testing_utils import ( + backend_empty_cache, + nightly, + numpy_cosine_similarity_distance, + require_torch_accelerator, + torch_device, +) + + +def download_single_file_checkpoint(repo_id, filename, tmpdir): + path = hf_hub_download(repo_id, filename=filename, local_dir=tmpdir) + return path + + +def download_original_config(config_url, tmpdir): + original_config_file = BytesIO(requests.get(config_url).content) + path = f"{tmpdir}/config.yaml" + with open(path, "wb") as f: + f.write(original_config_file.read()) + + return path + + +def download_diffusers_config(repo_id, tmpdir): + path = snapshot_download( + repo_id, + ignore_patterns=[ + "**/*.ckpt", + "*.ckpt", + "**/*.bin", + "*.bin", + "**/*.pt", + "*.pt", + "**/*.safetensors", + "*.safetensors", + ], + allow_patterns=["**/*.json", "*.json", "*.txt", "**/*.txt"], + local_dir=tmpdir, + ) + return path + + +@nightly +@require_torch_accelerator +class SingleFileModelTesterMixin: + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def test_single_file_model_config(self): + pretrained_kwargs = {} + single_file_kwargs = {} + + if hasattr(self, "subfolder") and self.subfolder: + pretrained_kwargs["subfolder"] = self.subfolder + + if hasattr(self, "torch_dtype") and self.torch_dtype: + pretrained_kwargs["torch_dtype"] = self.torch_dtype + single_file_kwargs["torch_dtype"] = self.torch_dtype + + model = self.model_class.from_pretrained(self.repo_id, **pretrained_kwargs) + model_single_file = self.model_class.from_single_file(self.ckpt_path, **single_file_kwargs) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between pretrained loading and single file loading" + ) + + def test_single_file_model_parameters(self): + pretrained_kwargs = {} + single_file_kwargs = {} + + if hasattr(self, "subfolder") and self.subfolder: + pretrained_kwargs["subfolder"] = self.subfolder + + if hasattr(self, "torch_dtype") and self.torch_dtype: + pretrained_kwargs["torch_dtype"] = self.torch_dtype + single_file_kwargs["torch_dtype"] = self.torch_dtype + + model = self.model_class.from_pretrained(self.repo_id, **pretrained_kwargs) + model_single_file = self.model_class.from_single_file(self.ckpt_path, **single_file_kwargs) + + state_dict = model.state_dict() + state_dict_single_file = model_single_file.state_dict() + + assert set(state_dict.keys()) == set(state_dict_single_file.keys()), ( + "Model parameters keys differ between pretrained and single file loading" + ) + + for key in state_dict.keys(): + param = state_dict[key] + param_single_file = state_dict_single_file[key] + + assert param.shape == param_single_file.shape, ( + f"Parameter shape mismatch for {key}: " + f"pretrained {param.shape} vs single file {param_single_file.shape}" + ) + + assert torch.allclose(param, param_single_file, rtol=1e-5, atol=1e-5), ( + f"Parameter values differ for {key}: " + f"max difference {torch.max(torch.abs(param - param_single_file)).item()}" + ) + + def test_checkpoint_altered_keys_loading(self): + # Test loading with checkpoints that have altered keys + if not hasattr(self, "alternate_keys_ckpt_paths") or not self.alternate_keys_ckpt_paths: + return + + for ckpt_path in self.alternate_keys_ckpt_paths: + backend_empty_cache(torch_device) + + single_file_kwargs = {} + if hasattr(self, "torch_dtype") and self.torch_dtype: + single_file_kwargs["torch_dtype"] = self.torch_dtype + + model = self.model_class.from_single_file(ckpt_path, **single_file_kwargs) + + del model + gc.collect() + backend_empty_cache(torch_device) + + +class SDSingleFileTesterMixin: + single_file_kwargs = {} + + def _compare_component_configs(self, pipe, single_file_pipe): + for param_name, param_value in single_file_pipe.text_encoder.config.to_dict().items(): + if param_name in ["torch_dtype", "architectures", "_name_or_path"]: + continue + assert pipe.text_encoder.config.to_dict()[param_name] == param_value + + PARAMS_TO_IGNORE = [ + "torch_dtype", + "_name_or_path", + "architectures", + "_use_default_values", + "_diffusers_version", + ] + for component_name, component in single_file_pipe.components.items(): + if component_name in single_file_pipe._optional_components: + continue + + # skip testing transformer based components here + # skip text encoders / safety checkers since they have already been tested + if component_name in ["text_encoder", "tokenizer", "safety_checker", "feature_extractor"]: + continue + + assert component_name in pipe.components, f"single file {component_name} not found in pretrained pipeline" + assert isinstance(component, pipe.components[component_name].__class__), ( + f"single file {component.__class__.__name__} and pretrained {pipe.components[component_name].__class__.__name__} are not the same" + ) + + for param_name, param_value in component.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + + # Some pretrained configs will set upcast attention to None + # In single file loading it defaults to the value in the class __init__ which is False + if param_name == "upcast_attention" and pipe.components[component_name].config[param_name] is None: + pipe.components[component_name].config[param_name] = param_value + + assert pipe.components[component_name].config[param_name] == param_value, ( + f"single file {param_name}: {param_value} differs from pretrained {pipe.components[component_name].config[param_name]}" + ) + + def test_single_file_components(self, pipe=None, single_file_pipe=None): + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + self.ckpt_path, safety_checker=None + ) + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_components_local_files_only(self, pipe=None, single_file_pipe=None): + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + local_ckpt_path, safety_checker=None, local_files_only=True + ) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_components_with_original_config( + self, + pipe=None, + single_file_pipe=None, + ): + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + # Not possible to infer this value when original config is provided + # we just pass it in here otherwise this test will fail + upcast_attention = pipe.unet.config.upcast_attention + + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + self.ckpt_path, + original_config=self.original_config, + safety_checker=None, + upcast_attention=upcast_attention, + ) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_components_with_original_config_local_files_only( + self, + pipe=None, + single_file_pipe=None, + ): + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + # Not possible to infer this value when original config is provided + # we just pass it in here otherwise this test will fail + upcast_attention = pipe.unet.config.upcast_attention + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_original_config = download_original_config(self.original_config, tmpdir) + + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + local_ckpt_path, + original_config=local_original_config, + safety_checker=None, + upcast_attention=upcast_attention, + local_files_only=True, + ) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_format_inference_is_same_as_pretrained(self, expected_max_diff=1e-4): + sf_pipe = self.pipeline_class.from_single_file(self.ckpt_path, safety_checker=None, **self.single_file_kwargs) + sf_pipe.unet.set_attn_processor(AttnProcessor()) + sf_pipe.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs(torch_device) + image_single_file = sf_pipe(**inputs).images[0] + + pipe = self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + pipe.unet.set_attn_processor(AttnProcessor()) + pipe.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs(torch_device) + image = pipe(**inputs).images[0] + + max_diff = numpy_cosine_similarity_distance(image.flatten(), image_single_file.flatten()) + + assert max_diff < expected_max_diff, f"{image.flatten()} != {image_single_file.flatten()}" + + def test_single_file_components_with_diffusers_config( + self, + pipe=None, + single_file_pipe=None, + ): + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + self.ckpt_path, config=self.repo_id, safety_checker=None + ) + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_components_with_diffusers_config_local_files_only( + self, + pipe=None, + single_file_pipe=None, + ): + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) + + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + local_ckpt_path, config=local_diffusers_config, safety_checker=None, local_files_only=True + ) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_setting_pipeline_dtype_to_fp16( + self, + single_file_pipe=None, + ): + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + self.ckpt_path, torch_dtype=torch.float16 + ) + + for component_name, component in single_file_pipe.components.items(): + if not isinstance(component, torch.nn.Module): + continue + + assert component.dtype == torch.float16 + + +class SDXLSingleFileTesterMixin: + def _compare_component_configs(self, pipe, single_file_pipe): + # Skip testing the text_encoder for Refiner Pipelines + if pipe.text_encoder: + for param_name, param_value in single_file_pipe.text_encoder.config.to_dict().items(): + if param_name in ["torch_dtype", "architectures", "_name_or_path"]: + continue + assert pipe.text_encoder.config.to_dict()[param_name] == param_value + + for param_name, param_value in single_file_pipe.text_encoder_2.config.to_dict().items(): + if param_name in ["torch_dtype", "architectures", "_name_or_path"]: + continue + assert pipe.text_encoder_2.config.to_dict()[param_name] == param_value + + PARAMS_TO_IGNORE = [ + "torch_dtype", + "_name_or_path", + "architectures", + "_use_default_values", + "_diffusers_version", + ] + for component_name, component in single_file_pipe.components.items(): + if component_name in single_file_pipe._optional_components: + continue + + # skip text encoders since they have already been tested + if component_name in ["text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2"]: + continue + + # skip safety checker if it is not present in the pipeline + if component_name in ["safety_checker", "feature_extractor"]: + continue + + assert component_name in pipe.components, f"single file {component_name} not found in pretrained pipeline" + assert isinstance(component, pipe.components[component_name].__class__), ( + f"single file {component.__class__.__name__} and pretrained {pipe.components[component_name].__class__.__name__} are not the same" + ) + + for param_name, param_value in component.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + + # Some pretrained configs will set upcast attention to None + # In single file loading it defaults to the value in the class __init__ which is False + if param_name == "upcast_attention" and pipe.components[component_name].config[param_name] is None: + pipe.components[component_name].config[param_name] = param_value + + assert pipe.components[component_name].config[param_name] == param_value, ( + f"single file {param_name}: {param_value} differs from pretrained {pipe.components[component_name].config[param_name]}" + ) + + def test_single_file_components(self, pipe=None, single_file_pipe=None): + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + self.ckpt_path, safety_checker=None + ) + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + self._compare_component_configs( + pipe, + single_file_pipe, + ) + + def test_single_file_components_local_files_only( + self, + pipe=None, + single_file_pipe=None, + ): + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + local_ckpt_path, safety_checker=None, local_files_only=True + ) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_components_with_original_config( + self, + pipe=None, + single_file_pipe=None, + ): + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + # Not possible to infer this value when original config is provided + # we just pass it in here otherwise this test will fail + upcast_attention = pipe.unet.config.upcast_attention + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + self.ckpt_path, + original_config=self.original_config, + safety_checker=None, + upcast_attention=upcast_attention, + ) + + self._compare_component_configs( + pipe, + single_file_pipe, + ) + + def test_single_file_components_with_original_config_local_files_only( + self, + pipe=None, + single_file_pipe=None, + ): + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + # Not possible to infer this value when original config is provided + # we just pass it in here otherwise this test will fail + upcast_attention = pipe.unet.config.upcast_attention + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_original_config = download_original_config(self.original_config, tmpdir) + + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + local_ckpt_path, + original_config=local_original_config, + upcast_attention=upcast_attention, + safety_checker=None, + local_files_only=True, + ) + + self._compare_component_configs( + pipe, + single_file_pipe, + ) + + def test_single_file_components_with_diffusers_config( + self, + pipe=None, + single_file_pipe=None, + ): + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + self.ckpt_path, config=self.repo_id, safety_checker=None + ) + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_components_with_diffusers_config_local_files_only( + self, + pipe=None, + single_file_pipe=None, + ): + pipe = pipe or self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) + + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + local_ckpt_path, config=local_diffusers_config, safety_checker=None, local_files_only=True + ) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_format_inference_is_same_as_pretrained(self, expected_max_diff=1e-4): + sf_pipe = self.pipeline_class.from_single_file(self.ckpt_path, torch_dtype=torch.float16, safety_checker=None) + sf_pipe.unet.set_default_attn_processor() + sf_pipe.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs(torch_device) + image_single_file = sf_pipe(**inputs).images[0] + + pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16, safety_checker=None) + pipe.unet.set_default_attn_processor() + pipe.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs(torch_device) + image = pipe(**inputs).images[0] + + max_diff = numpy_cosine_similarity_distance(image.flatten(), image_single_file.flatten()) + + assert max_diff < expected_max_diff + + def test_single_file_setting_pipeline_dtype_to_fp16( + self, + single_file_pipe=None, + ): + single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file( + self.ckpt_path, torch_dtype=torch.float16 + ) + + for component_name, component in single_file_pipe.components.items(): + if not isinstance(component, torch.nn.Module): + continue + + assert component.dtype == torch.float16 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_lumina2_transformer.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_lumina2_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..bb5a0bf473b6e3539ef8f6836793f240a875842b --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_lumina2_transformer.py @@ -0,0 +1,38 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from diffusers import ( + Lumina2Transformer2DModel, +) + +from ..testing_utils import ( + enable_full_determinism, +) +from .single_file_testing_utils import SingleFileModelTesterMixin + + +enable_full_determinism() + + +class TestLumina2Transformer2DModelSingleFile(SingleFileModelTesterMixin): + model_class = Lumina2Transformer2DModel + ckpt_path = "https://huggingface.co/Comfy-Org/Lumina_Image_2.0_Repackaged/blob/main/split_files/diffusion_models/lumina_2_model_bf16.safetensors" + alternate_keys_ckpt_paths = [ + "https://huggingface.co/Comfy-Org/Lumina_Image_2.0_Repackaged/blob/main/split_files/diffusion_models/lumina_2_model_bf16.safetensors" + ] + + repo_id = "Alpha-VLLM/Lumina-Image-2.0" + subfolder = "transformer" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_autoencoder_dc_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_autoencoder_dc_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..444ca40469772b783e4e18248eea5627495b2d37 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_autoencoder_dc_single_file.py @@ -0,0 +1,99 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch + +from diffusers import ( + AutoencoderDC, +) + +from ..testing_utils import ( + enable_full_determinism, + load_hf_numpy, + numpy_cosine_similarity_distance, + torch_device, +) +from .single_file_testing_utils import SingleFileModelTesterMixin + + +enable_full_determinism() + + +class TestAutoencoderDCSingleFile(SingleFileModelTesterMixin): + model_class = AutoencoderDC + ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0/blob/main/model.safetensors" + repo_id = "mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers" + main_input_name = "sample" + base_precision = 1e-2 + + def get_file_format(self, seed, shape): + return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy" + + def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False): + dtype = torch.float16 if fp16 else torch.float32 + image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype) + return image + + def test_single_file_inference_same_as_pretrained(self): + model_1 = self.model_class.from_pretrained(self.repo_id).to(torch_device) + model_2 = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id).to(torch_device) + + image = self.get_sd_image(33) + + with torch.no_grad(): + sample_1 = model_1(image).sample + sample_2 = model_2(image).sample + + assert sample_1.shape == sample_2.shape + + output_slice_1 = sample_1.flatten().float().cpu() + output_slice_2 = sample_2.flatten().float().cpu() + + assert numpy_cosine_similarity_distance(output_slice_1, output_slice_2) < 1e-4 + + def test_single_file_in_type_variant_components(self): + # `in` variant checkpoints require passing in a `config` parameter + # in order to set the scaling factor correctly. + # `in` and `mix` variants have the same keys and we cannot automatically infer a scaling factor. + # We default to using the `mix` config + repo_id = "mit-han-lab/dc-ae-f128c512-in-1.0-diffusers" + ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0/blob/main/model.safetensors" + + model = self.model_class.from_pretrained(repo_id) + model_single_file = self.model_class.from_single_file(ckpt_path, config=repo_id) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between pretrained loading and single file loading" + ) + + def test_single_file_mix_type_variant_components(self): + repo_id = "mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers" + ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0/blob/main/model.safetensors" + + model = self.model_class.from_pretrained(repo_id) + model_single_file = self.model_class.from_single_file(ckpt_path, config=repo_id) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between pretrained loading and single file loading" + ) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_controlnet_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_controlnet_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..2fa81fe3ae559a23fb422b84f83576feafb85e2b --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_controlnet_single_file.py @@ -0,0 +1,52 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch + +from diffusers import ( + ControlNetModel, +) + +from ..testing_utils import ( + enable_full_determinism, +) +from .single_file_testing_utils import SingleFileModelTesterMixin + + +enable_full_determinism() + + +class TestControlNetModelSingleFile(SingleFileModelTesterMixin): + model_class = ControlNetModel + ckpt_path = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth" + repo_id = "lllyasviel/control_v11p_sd15_canny" + + def test_single_file_arguments(self): + model_default = self.model_class.from_single_file(self.ckpt_path) + + assert model_default.config.upcast_attention is False + assert model_default.dtype == torch.float32 + + torch_dtype = torch.float16 + upcast_attention = True + + model = self.model_class.from_single_file( + self.ckpt_path, + upcast_attention=upcast_attention, + torch_dtype=torch_dtype, + ) + assert model.config.upcast_attention == upcast_attention + assert model.dtype == torch_dtype diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_flux_transformer_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_flux_transformer_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..0642a71c57562269a6f3aadbf91a396a0426f39a --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_flux_transformer_single_file.py @@ -0,0 +1,47 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc + +from diffusers import ( + FluxTransformer2DModel, +) + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + torch_device, +) +from .single_file_testing_utils import SingleFileModelTesterMixin + + +enable_full_determinism() + + +class TestFluxTransformer2DModelSingleFile(SingleFileModelTesterMixin): + model_class = FluxTransformer2DModel + ckpt_path = "https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors" + alternate_keys_ckpt_paths = ["https://huggingface.co/Comfy-Org/flux1-dev/blob/main/flux1-dev-fp8.safetensors"] + + repo_id = "black-forest-labs/FLUX.1-dev" + subfolder = "transformer" + + def test_device_map_cuda(self): + backend_empty_cache(torch_device) + model = self.model_class.from_single_file(self.ckpt_path, device_map="cuda") + + del model + gc.collect() + backend_empty_cache(torch_device) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_motion_adapter_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_motion_adapter_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..a047c81b47aaf2dde3d4f6db70c81c90bd6306c4 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_motion_adapter_single_file.py @@ -0,0 +1,90 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from diffusers import ( + MotionAdapter, +) + +from ..testing_utils import ( + enable_full_determinism, +) + + +enable_full_determinism() + + +class MotionAdapterSingleFileTests: + model_class = MotionAdapter + + def test_single_file_components_version_v1_5(self): + ckpt_path = "https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15.ckpt" + repo_id = "guoyww/animatediff-motion-adapter-v1-5" + + model = self.model_class.from_pretrained(repo_id) + model_single_file = self.model_class.from_single_file(ckpt_path) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between pretrained loading and single file loading" + ) + + def test_single_file_components_version_v1_5_2(self): + ckpt_path = "https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt" + repo_id = "guoyww/animatediff-motion-adapter-v1-5-2" + + model = self.model_class.from_pretrained(repo_id) + model_single_file = self.model_class.from_single_file(ckpt_path) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between pretrained loading and single file loading" + ) + + def test_single_file_components_version_v1_5_3(self): + ckpt_path = "https://huggingface.co/guoyww/animatediff/blob/main/v3_sd15_mm.ckpt" + repo_id = "guoyww/animatediff-motion-adapter-v1-5-3" + + model = self.model_class.from_pretrained(repo_id) + model_single_file = self.model_class.from_single_file(ckpt_path) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between pretrained loading and single file loading" + ) + + def test_single_file_components_version_sdxl_beta(self): + ckpt_path = "https://huggingface.co/guoyww/animatediff/blob/main/mm_sdxl_v10_beta.ckpt" + repo_id = "guoyww/animatediff-motion-adapter-sdxl-beta" + + model = self.model_class.from_pretrained(repo_id) + model_single_file = self.model_class.from_single_file(ckpt_path) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between pretrained loading and single file loading" + ) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_sd_cascade_unet_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_sd_cascade_unet_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..7472122710ebaa22052b6ae03bcb29bcd425b0ec --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_sd_cascade_unet_single_file.py @@ -0,0 +1,114 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc + +import torch + +from diffusers import StableCascadeUNet +from diffusers.utils import logging + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + require_torch_accelerator, + slow, + torch_device, +) + + +logger = logging.get_logger(__name__) + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class StableCascadeUNetSingleFileTest: + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def test_single_file_components_stage_b(self): + model_single_file = StableCascadeUNet.from_single_file( + "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors", + torch_dtype=torch.bfloat16, + ) + model = StableCascadeUNet.from_pretrained( + "stabilityai/stable-cascade", variant="bf16", subfolder="decoder", use_safetensors=True + ) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between single file loading and pretrained loading" + ) + + def test_single_file_components_stage_b_lite(self): + model_single_file = StableCascadeUNet.from_single_file( + "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_lite_bf16.safetensors", + torch_dtype=torch.bfloat16, + ) + model = StableCascadeUNet.from_pretrained( + "stabilityai/stable-cascade", variant="bf16", subfolder="decoder_lite" + ) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between single file loading and pretrained loading" + ) + + def test_single_file_components_stage_c(self): + model_single_file = StableCascadeUNet.from_single_file( + "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors", + torch_dtype=torch.bfloat16, + ) + model = StableCascadeUNet.from_pretrained( + "stabilityai/stable-cascade-prior", variant="bf16", subfolder="prior" + ) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between single file loading and pretrained loading" + ) + + def test_single_file_components_stage_c_lite(self): + model_single_file = StableCascadeUNet.from_single_file( + "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_lite_bf16.safetensors", + torch_dtype=torch.bfloat16, + ) + model = StableCascadeUNet.from_pretrained( + "stabilityai/stable-cascade-prior", variant="bf16", subfolder="prior_lite" + ) + + PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"] + for param_name, param_value in model_single_file.config.items(): + if param_name in PARAMS_TO_IGNORE: + continue + assert model.config[param_name] == param_value, ( + f"{param_name} differs between single file loading and pretrained loading" + ) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_vae_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_vae_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..9198d9b16337326442bd3b6789001ae64da7b632 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_vae_single_file.py @@ -0,0 +1,91 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch + +from diffusers import ( + AutoencoderKL, +) + +from ..testing_utils import ( + enable_full_determinism, + load_hf_numpy, + numpy_cosine_similarity_distance, + torch_device, +) +from .single_file_testing_utils import SingleFileModelTesterMixin + + +enable_full_determinism() + + +class TestAutoencoderKLSingleFile(SingleFileModelTesterMixin): + model_class = AutoencoderKL + ckpt_path = ( + "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors" + ) + repo_id = "stabilityai/sd-vae-ft-mse" + main_input_name = "sample" + base_precision = 1e-2 + + def get_file_format(self, seed, shape): + return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy" + + def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False): + dtype = torch.float16 if fp16 else torch.float32 + image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype) + return image + + def test_single_file_inference_same_as_pretrained(self): + model_1 = self.model_class.from_pretrained(self.repo_id).to(torch_device) + model_2 = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id).to(torch_device) + + image = self.get_sd_image(33) + + generator = torch.Generator(torch_device) + + with torch.no_grad(): + sample_1 = model_1(image, generator=generator.manual_seed(0)).sample + sample_2 = model_2(image, generator=generator.manual_seed(0)).sample + + assert sample_1.shape == sample_2.shape + + output_slice_1 = sample_1.flatten().float().cpu() + output_slice_2 = sample_2.flatten().float().cpu() + + assert numpy_cosine_similarity_distance(output_slice_1, output_slice_2) < 1e-4 + + def test_single_file_arguments(self): + model_default = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id) + + assert model_default.config.scaling_factor == 0.18215 + assert model_default.config.sample_size == 256 + assert model_default.dtype == torch.float32 + + scaling_factor = 2.0 + sample_size = 512 + torch_dtype = torch.float16 + + model = self.model_class.from_single_file( + self.ckpt_path, + config=self.repo_id, + sample_size=sample_size, + scaling_factor=scaling_factor, + torch_dtype=torch_dtype, + ) + assert model.config.scaling_factor == scaling_factor + assert model.config.sample_size == sample_size + assert model.dtype == torch_dtype diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_wan_autoencoder_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_wan_autoencoder_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..0babf302348fa7950e0a1e5f8175f5e6c463ed58 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_wan_autoencoder_single_file.py @@ -0,0 +1,36 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from diffusers import ( + AutoencoderKLWan, +) + +from ..testing_utils import ( + enable_full_determinism, +) +from .single_file_testing_utils import SingleFileModelTesterMixin + + +enable_full_determinism() + + +class TestAutoencoderKLWanSingleFile(SingleFileModelTesterMixin): + model_class = AutoencoderKLWan + ckpt_path = ( + "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors" + ) + repo_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers" + subfolder = "vae" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_wan_transformer3d_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_wan_transformer3d_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..b76909206073952b8c3a4962435757dc27d42f87 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_model_wan_transformer3d_single_file.py @@ -0,0 +1,46 @@ +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch + +from diffusers import ( + WanTransformer3DModel, +) + +from ..testing_utils import ( + enable_full_determinism, + require_big_accelerator, +) +from .single_file_testing_utils import SingleFileModelTesterMixin + + +enable_full_determinism() + + +class TestWanTransformer3DModelText2VideoSingleFile(SingleFileModelTesterMixin): + model_class = WanTransformer3DModel + ckpt_path = "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_1.3B_bf16.safetensors" + repo_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers" + subfolder = "transformer" + + +@require_big_accelerator +class TestWanTransformer3DModelImage2VideoSingleFile(SingleFileModelTesterMixin): + model_class = WanTransformer3DModel + ckpt_path = "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors" + repo_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers" + torch_dtype = torch.float8_e4m3fn + subfolder = "transformer" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_sana_transformer.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_sana_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..9e2adb93bf2b7937473ae6c8f732f47bea509f6b --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_sana_transformer.py @@ -0,0 +1,24 @@ +from diffusers import ( + SanaTransformer2DModel, +) + +from ..testing_utils import ( + enable_full_determinism, +) +from .single_file_testing_utils import SingleFileModelTesterMixin + + +enable_full_determinism() + + +class TestSanaTransformer2DModelSingleFile(SingleFileModelTesterMixin): + model_class = SanaTransformer2DModel + ckpt_path = ( + "https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px/blob/main/checkpoints/Sana_1600M_1024px.pth" + ) + alternate_keys_ckpt_paths = [ + "https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px/blob/main/checkpoints/Sana_1600M_1024px.pth" + ] + + repo_id = "Efficient-Large-Model/Sana_1600M_1024px_diffusers" + subfolder = "transformer" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_img2img_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_img2img_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..141748b084a02dce07114a63df84b2b60e27f04e --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_img2img_single_file.py @@ -0,0 +1,194 @@ +import gc +import tempfile + +import torch + +from diffusers import ControlNetModel, StableDiffusionControlNetPipeline +from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import ( + SDSingleFileTesterMixin, + download_diffusers_config, + download_original_config, + download_single_file_checkpoint, +) + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionControlNetPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionControlNetPipeline + ckpt_path = ( + "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors" + ) + original_config = ( + "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" + ) + repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + init_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_img2img/sketch-mountains-input.png" + ) + control_image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" + ).resize((512, 512)) + prompt = "bird" + + inputs = { + "prompt": prompt, + "image": init_image, + "control_image": control_image, + "generator": generator, + "num_inference_steps": 3, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe.unet.set_default_attn_processor() + pipe.enable_model_cpu_offload(device=torch_device) + + pipe_sf = self.pipeline_class.from_single_file( + self.ckpt_path, + controlnet=controlnet, + ) + pipe_sf.unet.set_default_attn_processor() + pipe_sf.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs(torch_device) + output = pipe(**inputs).images[0] + + inputs = self.get_inputs(torch_device) + output_sf = pipe_sf(**inputs).images[0] + + max_diff = numpy_cosine_similarity_distance(output_sf.flatten(), output.flatten()) + assert max_diff < 1e-3 + + def test_single_file_components(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained( + self.repo_id, variant="fp16", safety_checker=None, controlnet=controlnet + ) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, + safety_checker=None, + controlnet=controlnet, + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_local_files_only(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weights_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weights_name, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_original_config(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, safety_checker=None, original_config=self.original_config + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_original_config_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + controlnet=controlnet, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weights_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weights_name, tmpdir) + + local_original_config = download_original_config(self.original_config, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + original_config=local_original_config, + controlnet=controlnet, + safety_checker=None, + local_files_only=True, + ) + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, safety_checker=None, original_config=self.original_config + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + controlnet=controlnet, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weights_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weights_name, tmpdir) + + local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + config=local_diffusers_config, + safety_checker=None, + controlnet=controlnet, + local_files_only=True, + ) + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_setting_pipeline_dtype_to_fp16(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16" + ) + single_file_pipe = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 + ) + super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_inpaint_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_inpaint_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..8238866cbfb39b6739158aefe292659e42390f75 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_inpaint_single_file.py @@ -0,0 +1,195 @@ +import gc +import tempfile + +import pytest +import torch + +from diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline +from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import ( + SDSingleFileTesterMixin, + download_diffusers_config, + download_original_config, + download_single_file_checkpoint, +) + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionControlNetInpaintPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionControlNetInpaintPipeline + ckpt_path = "https://huggingface.co/botp/stable-diffusion-v1-5-inpainting/blob/main/sd-v1-5-inpainting.ckpt" + original_config = "https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml" + repo_id = "stable-diffusion-v1-5/stable-diffusion-inpainting" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self): + control_image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" + ).resize((512, 512)) + image = load_image( + "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png" + ).resize((512, 512)) + mask_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_inpaint/input_bench_mask.png" + ).resize((512, 512)) + + inputs = { + "prompt": "bird", + "image": image, + "control_image": control_image, + "mask_image": mask_image, + "generator": torch.Generator(device="cpu").manual_seed(0), + "num_inference_steps": 3, + "output_type": "np", + } + + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet, safety_checker=None) + pipe.unet.set_default_attn_processor() + pipe.enable_model_cpu_offload(device=torch_device) + + pipe_sf = self.pipeline_class.from_single_file(self.ckpt_path, controlnet=controlnet, safety_checker=None) + pipe_sf.unet.set_default_attn_processor() + pipe_sf.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs() + output = pipe(**inputs).images[0] + + inputs = self.get_inputs() + output_sf = pipe_sf(**inputs).images[0] + + max_diff = numpy_cosine_similarity_distance(output_sf.flatten(), output.flatten()) + assert max_diff < 2e-3 + + def test_single_file_components(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained( + self.repo_id, variant="fp16", safety_checker=None, controlnet=controlnet + ) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, + safety_checker=None, + controlnet=controlnet, + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_local_files_only(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained(self.repo_id, safety_checker=None, controlnet=controlnet) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + @pytest.mark.skip(reason="runwayml original config repo does not exist") + def test_single_file_components_with_original_config(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, original_config=self.original_config + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + @pytest.mark.skip(reason="runwayml original config repo does not exist") + def test_single_file_components_with_original_config_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + controlnet=controlnet, + safety_checker=None, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_original_config = download_original_config(self.original_config, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + original_config=local_original_config, + controlnet=controlnet, + safety_checker=None, + local_files_only=True, + ) + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, + controlnet=controlnet, + config=self.repo_id, + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", + torch_dtype=torch.float16, + variant="fp16", + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + controlnet=controlnet, + safety_checker=None, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + config=local_diffusers_config, + controlnet=controlnet, + safety_checker=None, + local_files_only=True, + ) + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_setting_pipeline_dtype_to_fp16(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16" + ) + single_file_pipe = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 + ) + super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..80ef6c2574c2565c00fd7b50182d05550771b592 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_controlnet_single_file.py @@ -0,0 +1,182 @@ +import gc +import tempfile + +import torch + +from diffusers import ControlNetModel, StableDiffusionControlNetPipeline +from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import ( + SDSingleFileTesterMixin, + download_diffusers_config, + download_original_config, + download_single_file_checkpoint, +) + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionControlNetPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionControlNetPipeline + ckpt_path = ( + "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors" + ) + original_config = ( + "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" + ) + repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self): + control_image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" + ).resize((512, 512)) + inputs = { + "prompt": "bird", + "image": control_image, + "generator": torch.Generator(device="cpu").manual_seed(0), + "num_inference_steps": 3, + "output_type": "np", + } + + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe.unet.set_default_attn_processor() + pipe.enable_model_cpu_offload(device=torch_device) + + pipe_sf = self.pipeline_class.from_single_file( + self.ckpt_path, + controlnet=controlnet, + ) + pipe_sf.unet.set_default_attn_processor() + pipe_sf.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs() + output = pipe(**inputs).images[0] + + inputs = self.get_inputs() + output_sf = pipe_sf(**inputs).images[0] + + max_diff = numpy_cosine_similarity_distance(output_sf.flatten(), output.flatten()) + assert max_diff < 1e-3 + + def test_single_file_components(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained( + self.repo_id, variant="fp16", safety_checker=None, controlnet=controlnet + ) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, + safety_checker=None, + controlnet=controlnet, + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_local_files_only(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, controlnet=controlnet, local_files_only=True + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_original_config(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, original_config=self.original_config + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_original_config_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + controlnet=controlnet, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_original_config = download_original_config(self.original_config, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, original_config=local_original_config, controlnet=controlnet, local_files_only=True + ) + pipe_single_file.scheduler = pipe.scheduler + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config(self): + controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16") + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, safety_checker=None, config=self.repo_id + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + controlnet=controlnet, + safety_checker=None, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + config=local_diffusers_config, + controlnet=controlnet, + safety_checker=None, + local_files_only=True, + ) + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_setting_pipeline_dtype_to_fp16(self): + controlnet = ControlNetModel.from_pretrained( + "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16" + ) + single_file_pipe = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 + ) + super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_img2img_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_img2img_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..e76846c800a8804c0b00f10a1aa01ea7e4024fc3 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_img2img_single_file.py @@ -0,0 +1,98 @@ +import gc + +import torch + +from diffusers import ( + StableDiffusionImg2ImgPipeline, +) +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import SDSingleFileTesterMixin + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionImg2ImgPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionImg2ImgPipeline + ckpt_path = ( + "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors" + ) + original_config = ( + "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" + ) + repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + init_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_img2img/sketch-mountains-input.png" + ) + inputs = { + "prompt": "a fantasy landscape, concept art, high resolution", + "image": init_image, + "generator": generator, + "num_inference_steps": 3, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) + + +@slow +@require_torch_accelerator +class TestStableDiffusion21Img2ImgPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionImg2ImgPipeline + ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors" + original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml" + repo_id = "stabilityai/stable-diffusion-2-1" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + init_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_img2img/sketch-mountains-input.png" + ) + inputs = { + "prompt": "a fantasy landscape, concept art, high resolution", + "image": init_image, + "generator": generator, + "num_inference_steps": 3, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_inpaint_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_inpaint_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..6e5d27cdffef77daf0b72cf0358806d2728f0f44 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_inpaint_single_file.py @@ -0,0 +1,120 @@ +import gc + +import pytest +import torch + +from diffusers import ( + StableDiffusionInpaintPipeline, +) +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import SDSingleFileTesterMixin + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionInpaintPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionInpaintPipeline + ckpt_path = "https://huggingface.co/botp/stable-diffusion-v1-5-inpainting/blob/main/sd-v1-5-inpainting.ckpt" + original_config = "https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml" + repo_id = "botp/stable-diffusion-v1-5-inpainting" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + init_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_inpaint/input_bench_image.png" + ) + mask_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_inpaint/input_bench_mask.png" + ) + inputs = { + "prompt": "Face of a yellow cat, high resolution, sitting on a park bench", + "image": init_image, + "mask_image": mask_image, + "generator": generator, + "num_inference_steps": 3, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) + + def test_single_file_loading_4_channel_unet(self): + # Test loading single file inpaint with a 4 channel UNet + ckpt_path = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors" + pipe = self.pipeline_class.from_single_file(ckpt_path) + + assert pipe.unet.config.in_channels == 4 + + @pytest.mark.skip(reason="runwayml original config has been removed") + def test_single_file_components_with_original_config(self): + return + + @pytest.mark.skip(reason="runwayml original config has been removed") + def test_single_file_components_with_original_config_local_files_only(self): + return + + +@slow +@require_torch_accelerator +class TestStableDiffusion21InpaintPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionInpaintPipeline + ckpt_path = ( + "https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/blob/main/512-inpainting-ema.safetensors" + ) + original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inpainting-inference.yaml" + repo_id = "stabilityai/stable-diffusion-2-inpainting" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + init_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_inpaint/input_bench_image.png" + ) + mask_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_inpaint/input_bench_mask.png" + ) + inputs = { + "prompt": "Face of a yellow cat, high resolution, sitting on a park bench", + "image": init_image, + "mask_image": mask_image, + "generator": generator, + "num_inference_steps": 3, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..377dedbc573138ae3c657e25146d60efb17e197a --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_single_file.py @@ -0,0 +1,157 @@ +import gc +import tempfile + +import torch + +from diffusers import EulerDiscreteScheduler, StableDiffusionInstructPix2PixPipeline, StableDiffusionPipeline +from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + nightly, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import ( + SDSingleFileTesterMixin, + download_original_config, + download_single_file_checkpoint, +) + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionPipeline + ckpt_path = ( + "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors" + ) + original_config = ( + "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" + ) + repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + inputs = { + "prompt": "a fantasy landscape, concept art, high resolution", + "generator": generator, + "num_inference_steps": 2, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) + + def test_single_file_legacy_scheduler_loading(self): + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_original_config = download_original_config(self.original_config, tmpdir) + + pipe = self.pipeline_class.from_single_file( + local_ckpt_path, + original_config=local_original_config, + cache_dir=tmpdir, + local_files_only=True, + scheduler_type="euler", + ) + + # Default is PNDM for this checkpoint + assert isinstance(pipe.scheduler, EulerDiscreteScheduler) + + def test_single_file_legacy_scaling_factor(self): + new_scaling_factor = 10.0 + init_pipe = self.pipeline_class.from_single_file(self.ckpt_path) + pipe = self.pipeline_class.from_single_file(self.ckpt_path, scaling_factor=new_scaling_factor) + + assert init_pipe.vae.config.scaling_factor != new_scaling_factor + assert pipe.vae.config.scaling_factor == new_scaling_factor + + +@slow +class TestStableDiffusion21PipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionPipeline + ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors" + original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml" + repo_id = "stabilityai/stable-diffusion-2-1" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + inputs = { + "prompt": "a fantasy landscape, concept art, high resolution", + "generator": generator, + "num_inference_steps": 2, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) + + +@nightly +@slow +@require_torch_accelerator +class TestStableDiffusionInstructPix2PixPipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionInstructPix2PixPipeline + ckpt_path = "https://huggingface.co/timbrooks/instruct-pix2pix/blob/main/instruct-pix2pix-00-22000.safetensors" + original_config = ( + "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/refs/heads/main/configs/generate.yaml" + ) + repo_id = "timbrooks/instruct-pix2pix" + single_file_kwargs = {"extract_ema": True} + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" + ) + inputs = { + "prompt": "turn him into a cyborg", + "image": image, + "generator": generator, + "num_inference_steps": 3, + "guidance_scale": 7.5, + "image_guidance_scale": 1.0, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_upscale_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_upscale_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..ba4819fadf85945b988ceb8b5091449a7db4ea72 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_upscale_single_file.py @@ -0,0 +1,84 @@ +import gc + +import pytest +import torch + +from diffusers import ( + StableDiffusionUpscalePipeline, +) +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import SDSingleFileTesterMixin + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionUpscalePipelineSingleFileSlow(SDSingleFileTesterMixin): + pipeline_class = StableDiffusionUpscalePipeline + ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/blob/main/x4-upscaler-ema.safetensors" + original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml" + repo_id = "stabilityai/stable-diffusion-x4-upscaler" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def test_single_file_format_inference_is_same_as_pretrained(self): + image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" + "/sd2-upscale/low_res_cat.png" + ) + + prompt = "a cat sitting on a park bench" + pipe = StableDiffusionUpscalePipeline.from_pretrained(self.repo_id) + pipe.enable_model_cpu_offload(device=torch_device) + + generator = torch.Generator("cpu").manual_seed(0) + output = pipe(prompt=prompt, image=image, generator=generator, output_type="np", num_inference_steps=3) + image_from_pretrained = output.images[0] + + pipe_from_single_file = StableDiffusionUpscalePipeline.from_single_file(self.ckpt_path) + pipe_from_single_file.enable_model_cpu_offload(device=torch_device) + + generator = torch.Generator("cpu").manual_seed(0) + output_from_single_file = pipe_from_single_file( + prompt=prompt, image=image, generator=generator, output_type="np", num_inference_steps=3 + ) + image_from_single_file = output_from_single_file.images[0] + + assert image_from_pretrained.shape == (512, 512, 3) + assert image_from_single_file.shape == (512, 512, 3) + assert ( + numpy_cosine_similarity_distance(image_from_pretrained.flatten(), image_from_single_file.flatten()) < 1e-3 + ) + + @pytest.mark.xfail( + condition=True, + reason="Test fails because of mismatches in the configs but it is very hard to properly fix this considering downstream usecase.", + strict=True, + ) + def test_single_file_components_with_original_config(self): + super().test_single_file_components_with_original_config() + + @pytest.mark.xfail( + condition=True, + reason="Test fails because of mismatches in the configs but it is very hard to properly fix this considering downstream usecase.", + strict=True, + ) + def test_single_file_components_with_original_config_local_files_only(self): + super().test_single_file_components_with_original_config_local_files_only() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_adapter_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_adapter_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..3d124fa8c23ca22f44571f6d0387eb537a8566aa --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_adapter_single_file.py @@ -0,0 +1,210 @@ +import gc +import tempfile + +import torch + +from diffusers import ( + StableDiffusionXLAdapterPipeline, + T2IAdapter, +) +from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import ( + SDXLSingleFileTesterMixin, + download_diffusers_config, + download_original_config, + download_single_file_checkpoint, +) + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionXLAdapterPipelineSingleFileSlow(SDXLSingleFileTesterMixin): + pipeline_class = StableDiffusionXLAdapterPipeline + ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors" + repo_id = "stabilityai/stable-diffusion-xl-base-1.0" + original_config = ( + "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml" + ) + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self): + prompt = "toy" + generator = torch.Generator(device="cpu").manual_seed(0) + image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png" + ) + + inputs = { + "prompt": prompt, + "image": image, + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 7.5, + "output_type": "np", + } + + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16) + pipe_single_file = StableDiffusionXLAdapterPipeline.from_single_file( + self.ckpt_path, + adapter=adapter, + torch_dtype=torch.float16, + safety_checker=None, + ) + pipe_single_file.enable_model_cpu_offload(device=torch_device) + pipe_single_file.set_progress_bar_config(disable=None) + + inputs = self.get_inputs() + images_single_file = pipe_single_file(**inputs).images[0] + + pipe = StableDiffusionXLAdapterPipeline.from_pretrained( + self.repo_id, + adapter=adapter, + torch_dtype=torch.float16, + safety_checker=None, + ) + pipe.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs() + images = pipe(**inputs).images[0] + + assert images_single_file.shape == (768, 512, 3) + assert images.shape == (768, 512, 3) + + max_diff = numpy_cosine_similarity_distance(images.flatten(), images_single_file.flatten()) + assert max_diff < 5e-3 + + def test_single_file_components(self): + adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + adapter=adapter, + torch_dtype=torch.float16, + ) + + pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, safety_checker=None, adapter=adapter) + super().test_single_file_components(pipe, pipe_single_file) + + def test_single_file_components_local_files_only(self): + adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + adapter=adapter, + torch_dtype=torch.float16, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + + single_file_pipe = self.pipeline_class.from_single_file( + local_ckpt_path, adapter=adapter, safety_checker=None, local_files_only=True + ) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_components_with_diffusers_config(self): + adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + adapter=adapter, + torch_dtype=torch.float16, + safety_checker=None, + ) + + pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, config=self.repo_id, adapter=adapter) + self._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config_local_files_only(self): + adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + adapter=adapter, + torch_dtype=torch.float16, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + config=local_diffusers_config, + adapter=adapter, + safety_checker=None, + local_files_only=True, + ) + self._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_original_config(self): + adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + adapter=adapter, + torch_dtype=torch.float16, + safety_checker=None, + ) + + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, original_config=self.original_config, adapter=adapter + ) + self._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_original_config_local_files_only(self): + adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + adapter=adapter, + torch_dtype=torch.float16, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_original_config = download_original_config(self.original_config, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + original_config=local_original_config, + adapter=adapter, + safety_checker=None, + local_files_only=True, + ) + self._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_setting_pipeline_dtype_to_fp16(self): + adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16) + + single_file_pipe = self.pipeline_class.from_single_file( + self.ckpt_path, adapter=adapter, torch_dtype=torch.float16 + ) + super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_controlnet_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_controlnet_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..6f503702610abf791b035e6f595b8617861561a8 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_controlnet_single_file.py @@ -0,0 +1,205 @@ +import gc +import tempfile + +import torch + +from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline +from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import ( + SDXLSingleFileTesterMixin, + download_diffusers_config, + download_single_file_checkpoint, +) + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionXLControlNetPipelineSingleFileSlow(SDXLSingleFileTesterMixin): + pipeline_class = StableDiffusionXLControlNetPipeline + ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors" + repo_id = "stabilityai/stable-diffusion-xl-base-1.0" + original_config = ( + "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml" + ) + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png" + ) + inputs = { + "prompt": "Stormtrooper's lecture", + "image": image, + "generator": generator, + "num_inference_steps": 2, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, torch_dtype=torch.float16 + ) + pipe_single_file.unet.set_default_attn_processor() + pipe_single_file.enable_model_cpu_offload(device=torch_device) + pipe_single_file.set_progress_bar_config(disable=None) + + inputs = self.get_inputs(torch_device) + single_file_images = pipe_single_file(**inputs).images[0] + + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet, torch_dtype=torch.float16) + pipe.unet.set_default_attn_processor() + pipe.enable_model_cpu_offload(device=torch_device) + + inputs = self.get_inputs(torch_device) + images = pipe(**inputs).images[0] + + assert images.shape == (512, 512, 3) + assert single_file_images.shape == (512, 512, 3) + + max_diff = numpy_cosine_similarity_distance(images[0].flatten(), single_file_images[0].flatten()) + assert max_diff < 5e-2 + + def test_single_file_components(self): + controlnet = ControlNetModel.from_pretrained( + "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + controlnet=controlnet, + torch_dtype=torch.float16, + ) + + pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, controlnet=controlnet) + super().test_single_file_components(pipe, pipe_single_file) + + def test_single_file_components_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + controlnet=controlnet, + torch_dtype=torch.float16, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + + single_file_pipe = self.pipeline_class.from_single_file( + local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True + ) + + self._compare_component_configs(pipe, single_file_pipe) + + def test_single_file_components_with_original_config(self): + controlnet = ControlNetModel.from_pretrained( + "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + controlnet=controlnet, + torch_dtype=torch.float16, + ) + + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, + original_config=self.original_config, + controlnet=controlnet, + ) + self._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_original_config_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + variant="fp16", + controlnet=controlnet, + torch_dtype=torch.float16, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + safety_checker=None, + controlnet=controlnet, + local_files_only=True, + ) + self._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config(self): + controlnet = ControlNetModel.from_pretrained( + "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) + pipe_single_file = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, config=self.repo_id + ) + + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_components_with_diffusers_config_local_files_only(self): + controlnet = ControlNetModel.from_pretrained( + "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" + ) + pipe = self.pipeline_class.from_pretrained( + self.repo_id, + controlnet=controlnet, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) + local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) + local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) + + pipe_single_file = self.pipeline_class.from_single_file( + local_ckpt_path, + config=local_diffusers_config, + safety_checker=None, + controlnet=controlnet, + local_files_only=True, + ) + super()._compare_component_configs(pipe, pipe_single_file) + + def test_single_file_setting_pipeline_dtype_to_fp16(self): + controlnet = ControlNetModel.from_pretrained( + "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" + ) + single_file_pipe = self.pipeline_class.from_single_file( + self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 + ) + super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_img2img_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_img2img_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..56657f37d912c52704af428704e85f69962f8937 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_img2img_single_file.py @@ -0,0 +1,104 @@ +import gc + +import torch + +from diffusers import ( + DDIMScheduler, + StableDiffusionXLImg2ImgPipeline, +) +from diffusers.utils import load_image + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import SDXLSingleFileTesterMixin + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionXLImg2ImgPipelineSingleFileSlow(SDXLSingleFileTesterMixin): + pipeline_class = StableDiffusionXLImg2ImgPipeline + ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors" + repo_id = "stabilityai/stable-diffusion-xl-base-1.0" + original_config = ( + "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml" + ) + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + init_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_img2img/sketch-mountains-input.png" + ) + inputs = { + "prompt": "a fantasy landscape, concept art, high resolution", + "image": init_image, + "generator": generator, + "num_inference_steps": 3, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) + + +@slow +@require_torch_accelerator +class StableDiffusionXLImg2ImgRefinerPipelineSingleFileSlowTests: + pipeline_class = StableDiffusionXLImg2ImgPipeline + ckpt_path = ( + "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors" + ) + repo_id = "stabilityai/stable-diffusion-xl-refiner-1.0" + original_config = ( + "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_refiner.yaml" + ) + + def test_single_file_format_inference_is_same_as_pretrained(self): + init_image = load_image( + "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" + "/stable_diffusion_img2img/sketch-mountains-input.png" + ) + + pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16) + pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) + pipe.unet.set_default_attn_processor() + pipe.enable_model_cpu_offload(device=torch_device) + + generator = torch.Generator(device="cpu").manual_seed(0) + image = pipe( + prompt="mountains", image=init_image, num_inference_steps=5, generator=generator, output_type="np" + ).images[0] + + pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, torch_dtype=torch.float16) + pipe_single_file.scheduler = DDIMScheduler.from_config(pipe_single_file.scheduler.config) + pipe_single_file.unet.set_default_attn_processor() + pipe_single_file.enable_model_cpu_offload(device=torch_device) + + generator = torch.Generator(device="cpu").manual_seed(0) + image_single_file = pipe_single_file( + prompt="mountains", image=init_image, num_inference_steps=5, generator=generator, output_type="np" + ).images[0] + + max_diff = numpy_cosine_similarity_distance(image.flatten(), image_single_file.flatten()) + + assert max_diff < 5e-4 diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_instruct_pix2pix.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_instruct_pix2pix.py new file mode 100644 index 0000000000000000000000000000000000000000..d755b70105163f4d433da8d46536347f4ad26f79 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_instruct_pix2pix.py @@ -0,0 +1,50 @@ +import gc + +import torch + +from diffusers import StableDiffusionXLInstructPix2PixPipeline + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + require_torch_accelerator, + slow, + torch_device, +) + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class StableDiffusionXLInstructPix2PixPipeline: + pipeline_class = StableDiffusionXLInstructPix2PixPipeline + ckpt_path = "https://huggingface.co/stabilityai/cosxl/blob/main/cosxl_edit.safetensors" + original_config = None + repo_id = "diffusers/sdxl-instructpix2pix-768" + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + inputs = { + "prompt": "a fantasy landscape, concept art, high resolution", + "generator": generator, + "num_inference_steps": 2, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_setting_cosxl_edit(self): + # Default is PNDM for this checkpoint + pipe = self.pipeline_class.from_single_file(self.ckpt_path, config=self.repo_id, is_cosxl_edit=True) + assert pipe.is_cosxl_edit is True diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_single_file.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_single_file.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5319ca25c77f83b17a3aa97340562313961225 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/single_file/test_stable_diffusion_xl_single_file.py @@ -0,0 +1,53 @@ +import gc + +import torch + +from diffusers import ( + StableDiffusionXLPipeline, +) + +from ..testing_utils import ( + backend_empty_cache, + enable_full_determinism, + require_torch_accelerator, + slow, + torch_device, +) +from .single_file_testing_utils import SDXLSingleFileTesterMixin + + +enable_full_determinism() + + +@slow +@require_torch_accelerator +class TestStableDiffusionXLPipelineSingleFileSlow(SDXLSingleFileTesterMixin): + pipeline_class = StableDiffusionXLPipeline + ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors" + repo_id = "stabilityai/stable-diffusion-xl-base-1.0" + original_config = ( + "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml" + ) + + def setup_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def teardown_method(self): + gc.collect() + backend_empty_cache(torch_device) + + def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): + generator = torch.Generator(device=generator_device).manual_seed(seed) + inputs = { + "prompt": "a fantasy landscape, concept art, high resolution", + "generator": generator, + "num_inference_steps": 2, + "strength": 0.75, + "guidance_scale": 7.5, + "output_type": "np", + } + return inputs + + def test_single_file_format_inference_is_same_as_pretrained(self): + super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/tasksmith_behavior.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/tasksmith_behavior.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b8ef1ac667370a04f5111ff80be068b283a6e1 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/tasksmith_behavior.py @@ -0,0 +1,442 @@ +import tempfile +import unittest +import torch + + +class TestAdjacentBehavior(unittest.TestCase): + def test_compute_snr_importable(self): + from diffusers.training_utils import compute_snr + self.assertTrue(callable(compute_snr)) + + +class BlockRefinementSchedulerTest(unittest.TestCase): + def _sched(self, **kw): + from diffusers import BlockRefinementScheduler + cfg = dict(block_length=32, num_inference_steps=8, threshold=0.95, editing_threshold=None, minimal_topk=1) + cfg.update(kw) + return BlockRefinementScheduler(**cfg) + + def test_set_timesteps(self): + s = self._sched(); s.set_timesteps(8) + self.assertEqual(s.num_inference_steps, 8) + self.assertEqual(len(s.timesteps), 8) + self.assertEqual(s.timesteps[0].item(), 7) + self.assertEqual(s.timesteps[-1].item(), 0) + + def test_set_timesteps_invalid(self): + s = self._sched() + with self.assertRaises(ValueError): s.set_timesteps(0) + + def test_get_num_transfer_tokens_even(self): + s = self._sched() + sc = s.get_num_transfer_tokens(32, 8) + self.assertEqual(sc.sum().item(), 32) + self.assertEqual(len(sc), 8) + self.assertTrue((sc == 4).all().item()) + + def test_get_num_transfer_tokens_remainder(self): + s = self._sched() + sc = s.get_num_transfer_tokens(10, 3) + self.assertEqual(sc.sum().item(), 10) + self.assertEqual(len(sc), 3) + self.assertEqual(sc[0].item(), 4) + self.assertEqual(sc[1].item(), 3) + self.assertEqual(sc[2].item(), 3) + + def test_transfer_schedule_created_on_set_timesteps(self): + s = self._sched(block_length=16) + s.set_timesteps(4) + expected = torch.arange(16).view(1, 16) + logits = torch.zeros(1, 16, 32) + logits.scatter_(2, expected.unsqueeze(-1), torch.linspace(1, 4, 16).view(1, 16, 1)) + sample = torch.full((1, 16), 31, dtype=torch.long) + for step in range(4): + out = s.step(logits, step, sample, mask_token_id=31, temperature=0.0, threshold=2.0) + self.assertEqual(out.transfer_index.sum().item(), 4) + self.assertEqual((out.prev_sample == 31).sum().item(), 12 - 4 * step) + sample = out.prev_sample + self.assertTrue(torch.equal(sample, expected)) + + def test_save_load_config_round_trip(self): + from diffusers import BlockRefinementScheduler + s = self._sched(block_length=64, threshold=0.8, editing_threshold=0.5, minimal_topk=2) + with tempfile.TemporaryDirectory() as d: + s.save_config(d) + L = BlockRefinementScheduler.from_pretrained(d) + self.assertEqual(L.config.block_length, 64) + self.assertEqual(L.config.threshold, 0.8) + self.assertEqual(L.config.editing_threshold, 0.5) + self.assertEqual(L.config.minimal_topk, 2) + + def test_from_config(self): + from diffusers import BlockRefinementScheduler + s = self._sched(block_length=16, threshold=0.7) + n = BlockRefinementScheduler.from_config(s.config) + self.assertEqual(n.config.block_length, 16) + self.assertEqual(n.config.threshold, 0.7) + + def test_step_commits_tokens(self): + s = self._sched(block_length=8); s.set_timesteps(2) + mask_id = 31 + sample = torch.full((1, 8), mask_id, dtype=torch.long) + logits = torch.zeros(1, 8, 32) + for i in range(8): logits[0, i, i] = 10.0 - i + out = s.step(model_output=logits, timestep=0, sample=sample, mask_token_id=mask_id, temperature=0.0, threshold=0.95, return_dict=True) + self.assertEqual(out.transfer_index[0].sum().item(), 4) + + def test_step_no_editing_by_default(self): + s = self._sched(block_length=4); s.set_timesteps(2) + sample = torch.tensor([[10, 20, 31, 31]], dtype=torch.long) + logits = torch.zeros(1, 4, 32); logits[0, :, 15] = 10.0 + out = s.step(model_output=logits, timestep=0, sample=sample, mask_token_id=31, temperature=0.0, editing_threshold=None, return_dict=True) + self.assertFalse(out.editing_transfer_index.any().item()) + self.assertFalse(out.transfer_index[0, 0].item()) + self.assertFalse(out.transfer_index[0, 1].item()) + + def test_step_editing_replaces_tokens(self): + s = self._sched(block_length=4); s.set_timesteps(2) + sample = torch.tensor([[10, 20, 31, 31]], dtype=torch.long) + logits = torch.zeros(1, 4, 32) + logits[0, 0, 15] = 20.0; logits[0, 1, 20] = 20.0 + logits[0, 2, 5] = 5.0; logits[0, 3, 6] = 5.0 + out = s.step(model_output=logits, timestep=0, sample=sample, mask_token_id=31, temperature=0.0, editing_threshold=0.5, return_dict=True) + self.assertTrue(out.editing_transfer_index[0, 0].item()) + self.assertFalse(out.editing_transfer_index[0, 1].item()) + + def test_step_prompt_mask_prevents_editing(self): + s = self._sched(block_length=4); s.set_timesteps(2) + sample = torch.tensor([[10, 20, 31, 31]], dtype=torch.long) + logits = torch.zeros(1, 4, 32); logits[0, :, 15] = 20.0 + pm = torch.tensor([True, True, False, False]) + out = s.step(model_output=logits, timestep=0, sample=sample, mask_token_id=31, temperature=0.0, editing_threshold=0.5, prompt_mask=pm, return_dict=True) + self.assertFalse(out.editing_transfer_index[0, 0].item()) + self.assertFalse(out.editing_transfer_index[0, 1].item()) + + def test_step_return_tuple(self): + s = self._sched(block_length=4); s.set_timesteps(2) + sample = torch.full((1, 4), 31, dtype=torch.long) + logits = torch.randn(1, 4, 32) + r = s.step(model_output=logits, timestep=0, sample=sample, mask_token_id=31, temperature=0.0, return_dict=False) + self.assertIsInstance(r, tuple); self.assertEqual(len(r), 5) + + def test_step_batched(self): + s = self._sched(block_length=4); s.set_timesteps(2) + mask_id = 31; sample = torch.full((3, 4), mask_id, dtype=torch.long) + logits = torch.randn(3, 4, 32) + out = s.step(model_output=logits, timestep=0, sample=sample, mask_token_id=mask_id, temperature=0.0, return_dict=True) + self.assertEqual(out.prev_sample.shape, (3, 4)) + self.assertEqual(out.transfer_index.shape, (3, 4)) + + def test_check_block_should_continue_finished(self): + s = self._sched(); s.set_timesteps(8) + r = s.check_block_should_continue(step_idx=0, masks_remaining=True, editing_enabled=False, editing_transfer_index=torch.zeros(2,32,dtype=torch.bool), post_steps=0, max_post_steps=16, finished=torch.tensor([True,True])) + self.assertFalse(r) + + def test_check_block_should_continue_no_masks_no_edits(self): + s = self._sched(); s.set_timesteps(8) + r = s.check_block_should_continue(step_idx=5, masks_remaining=False, editing_enabled=True, editing_transfer_index=torch.zeros(1,32,dtype=torch.bool), post_steps=1, max_post_steps=16, finished=torch.tensor([False])) + self.assertFalse(r) + + def test_check_block_should_continue_steps_exhausted(self): + s = self._sched(); s.set_timesteps(8) + r = s.check_block_should_continue(step_idx=8, masks_remaining=True, editing_enabled=False, editing_transfer_index=torch.zeros(1,32,dtype=torch.bool), post_steps=0, max_post_steps=16, finished=torch.tensor([False])) + self.assertFalse(r) + + def test_check_eos_finished_marks_batch(self): + from diffusers import BlockRefinementScheduler + cur_x = torch.tensor([[10,11,5,2,99,99]], dtype=torch.long) + st = torch.tensor([[0,0,0,2]], dtype=torch.long) + ft = torch.tensor([[False,False,False,True]]) + f = BlockRefinementScheduler.check_eos_finished(cur_x=cur_x, sampled_tokens=st, final_transfer=ft, finished=torch.tensor([False]), eos_token_id=2, mask_token_id=99, prompt_length=2) + self.assertTrue(f[0].item()) + + def test_check_eos_finished_ignores_when_masks_before_eos(self): + from diffusers import BlockRefinementScheduler + cur_x = torch.tensor([[10,11,99,2]], dtype=torch.long) + st = torch.tensor([[0,0]], dtype=torch.long) + ft = torch.tensor([[False,True]]) + f = BlockRefinementScheduler.check_eos_finished(cur_x=cur_x, sampled_tokens=st, final_transfer=ft, finished=torch.tensor([False]), eos_token_id=2, mask_token_id=99, prompt_length=2) + self.assertFalse(f[0].item()) + + def test_check_eos_finished_already_finished(self): + from diffusers import BlockRefinementScheduler + cur_x = torch.tensor([[10,11,5,6]], dtype=torch.long) + st = torch.tensor([[0,0]], dtype=torch.long) + ft = torch.tensor([[False,False]]) + f = BlockRefinementScheduler.check_eos_finished(cur_x=cur_x, sampled_tokens=st, final_transfer=ft, finished=torch.tensor([True]), eos_token_id=2, mask_token_id=99, prompt_length=2) + self.assertTrue(f[0].item()) + + def test_add_noise(self): + s = self._sched(block_length=4) + ids = torch.tensor([[1,2,3,4,5,6,7,8]], dtype=torch.long) + am = torch.ones_like(ids) + gen = torch.Generator().manual_seed(42) + noisy, noisy_rev, masked, masked_rev = s.add_noise(ids, am, prompt_length=2, block_length=4, mask_token_id=99, generator=gen) + self.assertFalse(masked[0,0].item()); self.assertFalse(masked[0,1].item()) + self.assertFalse(masked_rev[0,0].item()); self.assertFalse(masked_rev[0,1].item()) + self.assertTrue((noisy[masked]==99).all().item()) + self.assertTrue((noisy_rev[masked_rev]==99).all().item()) + combined = masked | masked_rev + non_prompt = torch.zeros_like(masked); non_prompt[0,2:] = True + self.assertTrue((combined[0,2:]==non_prompt[0,2:]).all().item()) + + +class TestTopPFiltering(unittest.TestCase): + def test_top_p_filtering(self): + token = _check_public_distribution(self, [1., 2., 3., 4.], [0., 0., 0., 1.], top_p=0.5) + self.assertEqual(token, 3) + + def test_top_p_filtering_none(self): + _check_public_distribution(self, [1., 2., 3.], _softmax_numbers([1., 2., 3.]), top_p=None) + + def test_top_p_filtering_one(self): + _check_public_distribution(self, [1., 2., 3.], _softmax_numbers([1., 2., 3.]), top_p=1.0) + + +class TestTopKFiltering(unittest.TestCase): + def test_top_k_filtering(self): + kept = _softmax_numbers([4., 3.]) + _check_public_distribution(self, [1., 4., 2., 3.], [0., kept[0], 0., kept[1]], top_k=2) + + def test_top_k_filtering_none(self): + _check_public_distribution(self, [1., 2., 3.], _softmax_numbers([1., 2., 3.]), top_k=None) + + def test_top_k_filtering_zero(self): + _check_public_distribution(self, [1., 2., 3.], _softmax_numbers([1., 2., 3.]), top_k=0) + + def test_top_k_filtering_large_k(self): + _check_public_distribution(self, [1., 2., 3.], _softmax_numbers([1., 2., 3.]), top_k=100) + + +class TestSampleFromLogits(unittest.TestCase): + def test_greedy_sampling(self): + token = _check_public_distribution(self, [1., 5., 2.], _softmax_numbers([1., 5., 2.]), temperature=0.0, sampling_method="greedy") + self.assertEqual(token, 1) + + def test_multinomial_sampling(self): + token = _check_public_distribution(self, [0., 1000., -1000.], [0., 1., 0.], sampling_method="multinomial") + self.assertEqual(token, 1) + + def test_temperature_scaling(self): + _check_public_distribution(self, [1., 2., 3.], _softmax_numbers([2., 4., 6.]), temperature=0.5, sampling_method="multinomial") + + def test_negative_temperature_raises(self): + with self.assertRaises(ValueError): + _sample_public([1., 2.], temperature=-1.0) + + +class _DummyOut: + def __init__(self, logits): self.logits = logits + +class _DummyCausalLM(torch.nn.Module): + def __init__(self, vocab_size): + super().__init__() + self.vocab_size = int(vocab_size) + self.register_buffer("_anchor", torch.empty(0)) + self.seen_input_ids = [] + @property + def dtype(self): return torch.float32 + @property + def device(self): return self._anchor.device + def forward(self, input_ids, attention_mask=None, position_ids=None, **kw): + self.seen_input_ids.append(input_ids.detach().clone()) + B, L = input_ids.shape + lg = torch.zeros(B, L, self.vocab_size, device=input_ids.device) + pos = torch.arange(L, device=input_ids.device, dtype=torch.float32).view(1,L,1) + tok = (torch.arange(L, device=input_ids.device) % (self.vocab_size-2)).view(1,L,1) + lg.scatter_(2, tok.expand(B,-1,-1), 1.0+pos.expand(B,-1,-1)*0.1) + return _DummyOut(lg) + +def _pipe(tok=None): + from diffusers import BlockRefinementScheduler, LLaDA2Pipeline + return LLaDA2Pipeline(model=_DummyCausalLM(32), scheduler=BlockRefinementScheduler(), tokenizer=tok) + +class LLaDA2PipelineTest(unittest.TestCase): + def test_pipeline_runs(self): + p = _pipe().to("cpu") + ids = torch.tensor([[5,6,7,8],[1,2,3,4]], dtype=torch.long) + out = p(input_ids=ids, use_chat_template=False, gen_length=24, block_length=8, num_inference_steps=8, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, mask_token_id=31, eos_token_id=None, output_type="seq") + self.assertEqual(out.sequences.shape, (2,24)); self.assertFalse((out.sequences==31).any().item()) + + def test_pipeline_return_tuple(self): + p = _pipe().to("cpu") + seq, txt = p(input_ids=torch.tensor([[5,6,7,8]],dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, mask_token_id=31, output_type="seq", return_dict=False) + self.assertEqual(seq.shape,(1,16)); self.assertIsNone(txt) + + def test_output_type_seq(self): + p = _pipe().to("cpu") + out = p(input_ids=torch.tensor([[5,6,7,8]],dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, mask_token_id=31, output_type="seq") + self.assertIsNotNone(out.sequences); self.assertEqual(out.sequences.shape,(1,16)); self.assertIsNone(out.texts) + + def test_output_type_text_without_tokenizer(self): + p = _pipe(tok=None).to("cpu") + out = p(input_ids=torch.tensor([[5,6,7,8]],dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, mask_token_id=31, output_type="text") + self.assertIsNotNone(out.sequences); self.assertIsNone(out.texts) + + def test_output_type_text_with_tokenizer(self): + tok = type("T",(),{"eos_token_id":None,"mask_token_id":31,"batch_decode":lambda s,seqs,**kw:[f"decoded_{len(x)}" for x in seqs]})() + p = _pipe(tok=tok).to("cpu") + out = p(input_ids=torch.tensor([[5,6,7,8]],dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, temperature=0.0, threshold=2.0, minimal_topk=1, eos_early_stop=False, output_type="text") + self.assertIsNotNone(out.sequences); self.assertIsNotNone(out.texts); self.assertEqual(len(out.texts),1); self.assertTrue(out.texts[0].startswith("decoded_")) + + def test_output_type_invalid_raises(self): + p = _pipe().to("cpu") + with self.assertRaises(ValueError): + p(input_ids=torch.tensor([[5,6,7,8]],dtype=torch.long), use_chat_template=False, gen_length=16, block_length=8, num_inference_steps=4, mask_token_id=31, output_type="invalid") + + def test_prepare_input_ids_from_tensor(self): + p = _pipe().to("cpu") + ids = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.long) + out = _generate_public(p, input_ids=ids) + self.assertEqual(out.sequences.shape, (2, 8)) + self.assertTrue(p.model.seen_input_ids) + self.assertTrue(torch.equal(p.model.seen_input_ids[0][:, :3], ids)) + + def test_prepare_input_ids_from_1d_tensor(self): + p = _pipe().to("cpu") + ids = torch.tensor([1, 2, 3], dtype=torch.long) + out = _generate_public(p, input_ids=ids) + self.assertEqual(out.sequences.shape, (1, 8)) + self.assertTrue(p.model.seen_input_ids) + self.assertTrue(torch.equal(p.model.seen_input_ids[0][:, :3], ids.unsqueeze(0))) + + def test_prepare_input_ids_no_tokenizer_raises(self): + with self.assertRaises(ValueError): + _generate_public(_pipe(tok=None), prompt="hi") + + def test_prepare_input_ids_both_prompt_and_messages_raises(self): + tok = type("T", (), {"eos_token_id": None, "mask_token_id": 31})() + with self.assertRaises(ValueError): + _generate_public(_pipe(tok=tok), prompt="hi", messages=[{"role": "user", "content": "hi"}]) + + def test_prepare_input_ids_neither_raises(self): + with self.assertRaises(ValueError): + _generate_public(_pipe()) + + +class TrainingTests(unittest.TestCase): + def test_confidence_aware_loss(self): + from diffusers.training_utils import compute_confidence_aware_loss + logits = torch.tensor([[[5.,0.],[0.,1.]]]) + labels = torch.tensor([[0,0]]) + weights = torch.tensor([[1.,2.]]) + loss, loss_sft, loss_conf = compute_confidence_aware_loss(logits, labels, lambda_conf=0.0, per_token_weights=weights) + self.assertTrue(torch.allclose(loss, loss_sft)) + self.assertTrue(torch.allclose(loss_conf, torch.zeros_like(loss_conf))) + lc = 0.25 + loss, loss_sft, loss_conf = compute_confidence_aware_loss(logits, labels, lambda_conf=lc, per_token_weights=weights) + nll = torch.nn.functional.cross_entropy(logits.view(-1,2), labels.view(-1), reduction="none").view(1,2) + esft = (nll*weights).sum()/weights.sum() + pred = logits.argmax(dim=-1); correct = pred.eq(labels) + lp = torch.log_softmax(logits.float(), dim=-1); pr = lp.exp() + ent = -(pr*lp).sum(dim=-1).to(dtype=logits.dtype) + econf = (ent*weights*correct.to(ent.dtype)).sum()/(weights*correct.to(weights.dtype)).sum().clamp_min(1) + self.assertTrue(torch.allclose(loss_sft, esft)) + self.assertTrue(torch.allclose(loss_conf, econf)) + self.assertTrue(torch.allclose(loss, esft+lc*econf)) + lt, lstt, lct = compute_confidence_aware_loss(logits, labels, lambda_conf=lc, temperature=0.5, per_token_weights=weights) + self.assertTrue(torch.allclose(lstt, esft)) + self.assertFalse(torch.allclose(lct, econf)) + self.assertTrue(torch.allclose(lt, lstt+lc*lct)) + + + +def _softmax_numbers(values): + import math + peak = max(values) + weights = [math.exp(value - peak) for value in values] + return [weight / sum(weights) for weight in weights] + + +def _sample_public(values, **options): + from diffusers import BlockRefinementScheduler + # A dedicated mask vocabulary entry has zero probability and is never sampled. + logits = torch.tensor([[values + [-float("inf")]]], dtype=torch.float32) + mask_id = len(values) + scheduler = BlockRefinementScheduler(block_length=1, num_inference_steps=1) + scheduler.set_timesteps(1) + settings = dict(temperature=1.0, sampling_method="multinomial", threshold=2.0, + generator=torch.Generator().manual_seed(42)) + settings.update(options) + return scheduler.step(logits, 0, torch.tensor([[mask_id]], dtype=torch.long), + mask_token_id=mask_id, **settings) + + +def _check_public_distribution(case, values, expected, **options): + out = _sample_public(values, **options) + token = out.sampled_tokens[0, 0].item() + case.assertIn(token, [index for index, prob in enumerate(expected) if prob > 0.0]) + case.assertAlmostEqual(out.sampled_probs[0, 0].item(), expected[token], places=6) + case.assertEqual(out.prev_sample[0, 0].item(), token) + return token + + +def _generate_public(pipeline, **inputs): + return pipeline(**inputs, use_chat_template=False, gen_length=8, block_length=4, + num_inference_steps=2, temperature=0.0, threshold=2.0, + editing_threshold=None, minimal_topk=1, eos_early_stop=False, + mask_token_id=31, eos_token_id=None, output_type="seq") + + +class TestCoreTokenBehavior(unittest.TestCase): + def test_greedy_batched_commit_values_and_remaining_mask(self): + from diffusers import BlockRefinementScheduler + import math + scheduler = BlockRefinementScheduler(block_length=4, num_inference_steps=2) + scheduler.set_timesteps(2) + tokens = torch.tensor([[1, 2, 0, 3], [2, 1, 3, 0]], dtype=torch.long) + heights = torch.tensor([[1., 4., 2., 3.], [3., 1., 4., 2.]]) + logits = torch.zeros(2, 4, 5) + logits.scatter_(2, tokens.unsqueeze(-1), heights.unsqueeze(-1)) + expected_probs = torch.tensor([[math.exp(value) / (math.exp(value) + 4) + for value in row] for row in heights.tolist()]) + sample = torch.full((2, 4), 4, dtype=torch.long) + initial = sample.clone() + first = scheduler.step(logits, 0, sample, mask_token_id=4, temperature=0.0, + threshold=2.0, sampling_method="greedy") + expected_transfer = torch.tensor([[False, True, False, True], [True, False, True, False]]) + self.assertTrue(torch.equal(first.sampled_tokens, tokens)) + torch.testing.assert_close(first.sampled_probs, expected_probs, rtol=1e-6, atol=1e-7) + self.assertTrue(torch.equal(first.transfer_index, expected_transfer)) + self.assertFalse(first.editing_transfer_index.any().item()) + self.assertTrue(torch.equal(first.prev_sample, torch.tensor([[4, 2, 4, 3], [2, 4, 3, 4]]))) + self.assertTrue(torch.equal(sample, initial)) + second = scheduler.step(logits, 1, first.prev_sample, mask_token_id=4, temperature=0.0, + threshold=2.0, sampling_method="greedy") + self.assertTrue(torch.equal(second.transfer_index, ~expected_transfer)) + self.assertTrue(torch.equal(second.prev_sample, tokens)) + + def test_editing_writeback_respects_confidence_prompt_and_unchanged_tokens(self): + from diffusers import BlockRefinementScheduler + scheduler = BlockRefinementScheduler(block_length=4, num_inference_steps=2) + scheduler.set_timesteps(2) + sample = torch.tensor([[1, 2, 4, 3], [1, 2, 4, 3]], dtype=torch.long) + initial = sample.clone() + tokens = torch.tensor([[0, 0, 1, 2], [2, 2, 3, 0]], dtype=torch.long) + heights = torch.tensor([[5., 5., 4., 0.1], [5., 5., 4., 5.]]) + logits = torch.zeros(2, 4, 5) + logits.scatter_(2, tokens.unsqueeze(-1), heights.unsqueeze(-1)) + out = scheduler.step(logits, 0, sample, mask_token_id=4, temperature=0.0, + threshold=2.0, editing_threshold=0.8, + prompt_mask=torch.tensor([True, False, False, False])) + self.assertTrue(torch.equal(out.sampled_tokens, tokens)) + self.assertTrue(torch.equal(out.transfer_index, torch.tensor([[False, False, True, False], + [False, False, True, False]]))) + self.assertTrue(torch.equal(out.editing_transfer_index, torch.tensor([[False, True, False, False], + [False, False, False, True]]))) + self.assertTrue(torch.equal(out.prev_sample, torch.tensor([[1, 0, 1, 3], [1, 2, 3, 0]]))) + self.assertTrue(torch.equal(sample, initial)) + + def test_pipeline_commits_the_model_predicted_token_values(self): + pipeline = _pipe().to("cpu") + prompt = torch.tensor([[11, 12, 13], [21, 22, 23]], dtype=torch.long) + out = _generate_public(pipeline, input_ids=prompt) + # The fixture model predicts absolute position modulo30; prompt length is3. + expected = torch.arange(3, 11).repeat(2, 1) + self.assertTrue(torch.equal(out.sequences, expected)) + self.assertIsNone(out.texts) + self.assertTrue(torch.equal(pipeline.model.seen_input_ids[0][:, :3], prompt)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/tests/testing_utils.py b/tasks/tasksmith-deb681b2c96c/tests/source/tests/testing_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..53c1b8aa26ce7012f96f12eaae6ccbd900b1ae01 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/tests/testing_utils.py @@ -0,0 +1,1787 @@ +import functools +import glob +import importlib +import importlib.metadata +import inspect +import io +import logging +import multiprocessing +import os +import random +import re +import struct +import sys +import tempfile +import time +import urllib.parse +from collections import UserDict +from contextlib import contextmanager +from io import BytesIO, StringIO +from pathlib import Path +from typing import TYPE_CHECKING, Any, Callable, Tuple, Union + +import numpy as np +import PIL.Image +import PIL.ImageOps +import pytest +import requests +from numpy.linalg import norm +from packaging import version + +from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT +from diffusers.utils.import_utils import ( + BACKENDS_MAPPING, + is_accelerate_available, + is_bitsandbytes_available, + is_compel_available, + is_flax_available, + is_gguf_available, + is_kernels_available, + is_note_seq_available, + is_nvidia_modelopt_version, + is_onnx_available, + is_opencv_available, + is_optimum_quanto_available, + is_peft_available, + is_timm_available, + is_torch_available, + is_torch_version, + is_torchao_available, + is_torchsde_available, + is_transformers_available, +) +from diffusers.utils.logging import get_logger + + +if is_torch_available(): + import torch + + IS_ROCM_SYSTEM = torch.version.hip is not None + IS_CUDA_SYSTEM = torch.version.cuda is not None + IS_XPU_SYSTEM = getattr(torch.version, "xpu", None) is not None +else: + IS_ROCM_SYSTEM = False + IS_CUDA_SYSTEM = False + IS_XPU_SYSTEM = False + +IS_GITHUB_ACTIONS = os.getenv("GITHUB_ACTIONS") == "true" and os.getenv("DIFFUSERS_IS_CI") == "yes" + +global_rng = random.Random() + +logger = get_logger(__name__) + +_required_peft_version = is_peft_available() and version.parse( + version.parse(importlib.metadata.version("peft")).base_version +) > version.parse("0.5") +_required_transformers_version = is_transformers_available() and version.parse( + version.parse(importlib.metadata.version("transformers")).base_version +) > version.parse("4.33") + +USE_PEFT_BACKEND = _required_peft_version and _required_transformers_version +BIG_GPU_MEMORY = int(os.getenv("BIG_GPU_MEMORY", 40)) + +if is_torch_available(): + import torch + + # Set a backend environment variable for any extra module import required for a custom accelerator + if "DIFFUSERS_TEST_BACKEND" in os.environ: + backend = os.environ["DIFFUSERS_TEST_BACKEND"] + try: + _ = importlib.import_module(backend) + except ModuleNotFoundError as e: + raise ModuleNotFoundError( + f"Failed to import `DIFFUSERS_TEST_BACKEND` '{backend}'! This should be the name of an installed module \ + to enable a specified backend.):\n{e}" + ) from e + + if "DIFFUSERS_TEST_DEVICE" in os.environ: + torch_device = os.environ["DIFFUSERS_TEST_DEVICE"] + try: + # try creating device to see if provided device is valid + _ = torch.device(torch_device) + except RuntimeError as e: + raise RuntimeError( + f"Unknown testing device specified by environment variable `DIFFUSERS_TEST_DEVICE`: {torch_device}" + ) from e + logger.info(f"torch_device overrode to {torch_device}") + else: + if torch.cuda.is_available(): + torch_device = "cuda" + elif torch.xpu.is_available(): + torch_device = "xpu" + else: + torch_device = "cpu" + is_torch_higher_equal_than_1_12 = version.parse( + version.parse(torch.__version__).base_version + ) >= version.parse("1.12") + + if is_torch_higher_equal_than_1_12: + # Some builds of torch 1.12 don't have the mps backend registered. See #892 for more details + mps_backend_registered = hasattr(torch.backends, "mps") + torch_device = "mps" if (mps_backend_registered and torch.backends.mps.is_available()) else torch_device + + from diffusers.utils.torch_utils import get_torch_cuda_device_capability + + +def torch_all_close(a, b, *args, **kwargs): + if not is_torch_available(): + raise ValueError("PyTorch needs to be installed to use this function.") + if not torch.allclose(a, b, *args, **kwargs): + assert False, f"Max diff is absolute {(a - b).abs().max()}. Diff tensor is {(a - b).abs()}." + return True + + +def assert_tensors_close( + actual: "torch.Tensor", + expected: "torch.Tensor", + atol: float = 1e-5, + rtol: float = 1e-5, + msg: str = "", +) -> None: + """ + Assert that two tensors are close within tolerance. + + Uses the same formula as torch.allclose: |actual - expected| <= atol + rtol * |expected| + Provides concise, actionable error messages without dumping full tensors. + + Args: + actual: The actual tensor from the computation. + expected: The expected tensor to compare against. + atol: Absolute tolerance. + rtol: Relative tolerance. + msg: Optional message prefix for the assertion error. + + Raises: + AssertionError: If tensors have different shapes or values exceed tolerance. + + Example: + >>> assert_tensors_close(output, expected_output, atol=1e-5, rtol=1e-5, msg="Forward pass") + """ + if not is_torch_available(): + raise ValueError("PyTorch needs to be installed to use this function.") + + if actual.shape != expected.shape: + raise AssertionError(f"{msg} Shape mismatch: actual {actual.shape} vs expected {expected.shape}") + + if not torch.allclose(actual, expected, atol=atol, rtol=rtol): + abs_diff = (actual - expected).abs() + max_diff = abs_diff.max().item() + + flat_idx = abs_diff.argmax().item() + max_idx = tuple(idx.item() for idx in torch.unravel_index(torch.tensor(flat_idx), actual.shape)) + + threshold = atol + rtol * expected.abs() + mismatched = (abs_diff > threshold).sum().item() + total = actual.numel() + + raise AssertionError( + f"{msg}\n" + f"Tensors not close! Mismatched elements: {mismatched}/{total} ({100 * mismatched / total:.1f}%)\n" + f" Max diff: {max_diff:.6e} at index {max_idx}\n" + f" Actual: {actual.flatten()[flat_idx].item():.6e}\n" + f" Expected: {expected.flatten()[flat_idx].item():.6e}\n" + f" atol: {atol:.6e}, rtol: {rtol:.6e}" + ) + + +def numpy_cosine_similarity_distance(a, b): + similarity = np.dot(a, b) / (norm(a) * norm(b)) + distance = 1.0 - similarity.mean() + + return distance + + +def check_if_dicts_are_equal(dict1, dict2): + dict1, dict2 = dict1.copy(), dict2.copy() + + for key, value in dict1.items(): + if isinstance(value, set): + dict1[key] = sorted(value) + for key, value in dict2.items(): + if isinstance(value, set): + dict2[key] = sorted(value) + + for key in dict1: + if key not in dict2: + return False + if dict1[key] != dict2[key]: + return False + + for key in dict2: + if key not in dict1: + return False + + return True + + +def print_tensor_test( + tensor, + limit_to_slices=None, + max_torch_print=None, + filename="test_corrections.txt", + expected_tensor_name="expected_slice", +): + if max_torch_print: + torch.set_printoptions(threshold=10_000) + + test_name = os.environ.get("PYTEST_CURRENT_TEST") + if not torch.is_tensor(tensor): + tensor = torch.from_numpy(tensor) + if limit_to_slices: + tensor = tensor[0, -3:, -3:, -1] + + tensor_str = str(tensor.detach().cpu().flatten().to(torch.float32)).replace("\n", "") + # format is usually: + # expected_slice = np.array([-0.5713, -0.3018, -0.9814, 0.04663, -0.879, 0.76, -1.734, 0.1044, 1.161]) + output_str = tensor_str.replace("tensor", f"{expected_tensor_name} = np.array") + test_file, test_class, test_fn = test_name.split("::") + test_fn = test_fn.split()[0] + with open(filename, "a") as f: + print("::".join([test_file, test_class, test_fn, output_str]), file=f) + + +def get_tests_dir(append_path=None): + """ + Args: + append_path: optional path to append to the tests dir path + Return: + The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is + joined after the `tests` dir the former is provided. + """ + # this function caller's __file__ + caller__file__ = inspect.stack()[1][1] + tests_dir = os.path.abspath(os.path.dirname(caller__file__)) + + while not tests_dir.endswith("tests"): + tests_dir = os.path.dirname(tests_dir) + + if append_path: + return Path(tests_dir, append_path).as_posix() + else: + return tests_dir + + +# Taken from the following PR: +# https://github.com/huggingface/accelerate/pull/1964 +def str_to_bool(value) -> int: + """ + Converts a string representation of truth to `True` (1) or `False` (0). True values are `y`, `yes`, `t`, `true`, + `on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`; + """ + value = value.lower() + if value in ("y", "yes", "t", "true", "on", "1"): + return 1 + elif value in ("n", "no", "f", "false", "off", "0"): + return 0 + else: + raise ValueError(f"invalid truth value {value}") + + +def parse_flag_from_env(key, default=False): + try: + value = os.environ[key] + except KeyError: + # KEY isn't set, default to `default`. + _value = default + else: + # KEY is set, convert it to True or False. + try: + _value = str_to_bool(value) + except ValueError: + # More values are supported, but let's keep the message simple. + raise ValueError(f"If set, {key} must be yes or no.") + return _value + + +_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False) +_run_nightly_tests = parse_flag_from_env("RUN_NIGHTLY", default=False) + + +def floats_tensor(shape, scale=1.0, rng=None, name=None): + """Creates a random float32 tensor""" + if rng is None: + rng = global_rng + + total_dims = 1 + for dim in shape: + total_dims *= dim + + values = [] + for _ in range(total_dims): + values.append(rng.random() * scale) + + return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous() + + +def slow(test_case): + """ + Decorator marking a test as slow. + + Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them. + + """ + return pytest.mark.skipif(not _run_slow_tests, reason="test is slow")(test_case) + + +def nightly(test_case): + """ + Decorator marking a test that runs nightly in the diffusers CI. + + Slow tests are skipped by default. Set the RUN_NIGHTLY environment variable to a truthy value to run them. + + """ + return pytest.mark.skipif(not _run_nightly_tests, reason="test is nightly")(test_case) + + +def is_torch_compile(test_case): + """ + Decorator marking a test as a torch.compile test. These tests can be filtered using: + pytest -m "not compile" to skip + pytest -m compile to run only these tests + """ + return pytest.mark.compile(test_case) + + +def is_single_file(test_case): + """ + Decorator marking a test as a single file loading test. These tests can be filtered using: + pytest -m "not single_file" to skip + pytest -m single_file to run only these tests + """ + return pytest.mark.single_file(test_case) + + +def is_lora(test_case): + """ + Decorator marking a test as a LoRA test. These tests can be filtered using: + pytest -m "not lora" to skip + pytest -m lora to run only these tests + """ + return pytest.mark.lora(test_case) + + +def is_ip_adapter(test_case): + """ + Decorator marking a test as an IP Adapter test. These tests can be filtered using: + pytest -m "not ip_adapter" to skip + pytest -m ip_adapter to run only these tests + """ + return pytest.mark.ip_adapter(test_case) + + +def is_training(test_case): + """ + Decorator marking a test as a training test. These tests can be filtered using: + pytest -m "not training" to skip + pytest -m training to run only these tests + """ + return pytest.mark.training(test_case) + + +def is_attention(test_case): + """ + Decorator marking a test as an attention test. These tests can be filtered using: + pytest -m "not attention" to skip + pytest -m attention to run only these tests + """ + return pytest.mark.attention(test_case) + + +def is_memory(test_case): + """ + Decorator marking a test as a memory optimization test. These tests can be filtered using: + pytest -m "not memory" to skip + pytest -m memory to run only these tests + """ + return pytest.mark.memory(test_case) + + +def is_cpu_offload(test_case): + """ + Decorator marking a test as a CPU offload test. These tests can be filtered using: + pytest -m "not cpu_offload" to skip + pytest -m cpu_offload to run only these tests + """ + return pytest.mark.cpu_offload(test_case) + + +def is_group_offload(test_case): + """ + Decorator marking a test as a group offload test. These tests can be filtered using: + pytest -m "not group_offload" to skip + pytest -m group_offload to run only these tests + """ + return pytest.mark.group_offload(test_case) + + +def is_quantization(test_case): + """ + Decorator marking a test as a quantization test. These tests can be filtered using: + pytest -m "not quantization" to skip + pytest -m quantization to run only these tests + """ + return pytest.mark.quantization(test_case) + + +def is_bitsandbytes(test_case): + """ + Decorator marking a test as a BitsAndBytes quantization test. These tests can be filtered using: + pytest -m "not bitsandbytes" to skip + pytest -m bitsandbytes to run only these tests + """ + return pytest.mark.bitsandbytes(test_case) + + +def is_quanto(test_case): + """ + Decorator marking a test as a Quanto quantization test. These tests can be filtered using: + pytest -m "not quanto" to skip + pytest -m quanto to run only these tests + """ + return pytest.mark.quanto(test_case) + + +def is_torchao(test_case): + """ + Decorator marking a test as a TorchAO quantization test. These tests can be filtered using: + pytest -m "not torchao" to skip + pytest -m torchao to run only these tests + """ + return pytest.mark.torchao(test_case) + + +def is_gguf(test_case): + """ + Decorator marking a test as a GGUF quantization test. These tests can be filtered using: + pytest -m "not gguf" to skip + pytest -m gguf to run only these tests + """ + return pytest.mark.gguf(test_case) + + +def is_modelopt(test_case): + """ + Decorator marking a test as a NVIDIA ModelOpt quantization test. These tests can be filtered using: + pytest -m "not modelopt" to skip + pytest -m modelopt to run only these tests + """ + return pytest.mark.modelopt(test_case) + + +def is_context_parallel(test_case): + """ + Decorator marking a test as a context parallel inference test. These tests can be filtered using: + pytest -m "not context_parallel" to skip + pytest -m context_parallel to run only these tests + """ + return pytest.mark.context_parallel(test_case) + + +def is_cache(test_case): + """ + Decorator marking a test as a cache test. These tests can be filtered using: + pytest -m "not cache" to skip + pytest -m cache to run only these tests + """ + return pytest.mark.cache(test_case) + + +def require_torch(test_case): + """ + Decorator marking a test that requires PyTorch. These tests are skipped when PyTorch isn't installed. + """ + return pytest.mark.skipif(not is_torch_available(), reason="test requires PyTorch")(test_case) + + +def require_torch_2(test_case): + """ + Decorator marking a test that requires PyTorch 2. These tests are skipped when it isn't installed. + """ + return pytest.mark.skipif( + not (is_torch_available() and is_torch_version(">=", "2.0.0")), reason="test requires PyTorch 2" + )(test_case) + + +def require_torch_version_greater_equal(torch_version): + """Decorator marking a test that requires torch with a specific version or greater.""" + + def decorator(test_case): + correct_torch_version = is_torch_available() and is_torch_version(">=", torch_version) + return pytest.mark.skipif( + not correct_torch_version, + reason=f"test requires torch with the version greater than or equal to {torch_version}", + )(test_case) + + return decorator + + +def require_torch_version_greater(torch_version): + """Decorator marking a test that requires torch with a specific version greater.""" + + def decorator(test_case): + correct_torch_version = is_torch_available() and is_torch_version(">", torch_version) + return pytest.mark.skipif( + not correct_torch_version, reason=f"test requires torch with the version greater than {torch_version}" + )(test_case) + + return decorator + + +def require_torch_gpu(test_case): + """Decorator marking a test that requires CUDA and PyTorch.""" + return pytest.mark.skipif(torch_device != "cuda", reason="test requires PyTorch+CUDA")(test_case) + + +def require_torch_cuda_compatibility(expected_compute_capability): + def decorator(test_case): + if torch.cuda.is_available(): + current_compute_capability = get_torch_cuda_device_capability() + return pytest.mark.skipif( + float(current_compute_capability) != float(expected_compute_capability), + reason="Test not supported for this compute capability.", + )(test_case) + return test_case + + return decorator + + +# These decorators are for accelerator-specific behaviours that are not GPU-specific +def require_torch_accelerator(test_case): + """Decorator marking a test that requires an accelerator backend and PyTorch.""" + return pytest.mark.skipif(torch_device == "cpu", reason="test requires accelerator+PyTorch")(test_case) + + +def require_torch_multi_gpu(test_case): + """ + Decorator marking a test that requires a multi-GPU setup (in PyTorch). These tests are skipped on a machine without + multiple GPUs. To run *only* the multi_gpu tests, assuming all test names contain multi_gpu: $ pytest -sv ./tests + -k "multi_gpu" + """ + if not is_torch_available(): + return pytest.mark.skip(reason="test requires PyTorch")(test_case) + + import torch + + return pytest.mark.skipif(torch.cuda.device_count() <= 1, reason="test requires multiple GPUs")(test_case) + + +def require_torch_multi_accelerator(test_case): + """ + Decorator marking a test that requires a multi-accelerator setup (in PyTorch). These tests are skipped on a machine + without multiple hardware accelerators. + """ + if not is_torch_available(): + return pytest.mark.skip(reason="test requires PyTorch")(test_case) + + import torch + + return pytest.mark.skipif( + not (torch.cuda.device_count() > 1 or torch.xpu.device_count() > 1), + reason="test requires multiple hardware accelerators", + )(test_case) + + +def require_torch_accelerator_with_fp16(test_case): + """Decorator marking a test that requires an accelerator with support for the FP16 data type.""" + return pytest.mark.skipif( + not _is_torch_fp16_available(torch_device), reason="test requires accelerator with fp16 support" + )(test_case) + + +def require_torch_accelerator_with_fp64(test_case): + """Decorator marking a test that requires an accelerator with support for the FP64 data type.""" + return pytest.mark.skipif( + not _is_torch_fp64_available(torch_device), reason="test requires accelerator with fp64 support" + )(test_case) + + +def require_big_gpu_with_torch_cuda(test_case): + """ + Decorator marking a test that requires a bigger GPU (24GB) for execution. Some example pipelines: Flux, SD3, Cog, + etc. + """ + if not is_torch_available(): + return pytest.mark.skip(reason="test requires PyTorch")(test_case) + + import torch + + if not torch.cuda.is_available(): + return pytest.mark.skip(reason="test requires PyTorch CUDA")(test_case) + + device_properties = torch.cuda.get_device_properties(0) + total_memory = device_properties.total_memory / (1024**3) + return pytest.mark.skipif( + total_memory < BIG_GPU_MEMORY, reason=f"test requires a GPU with at least {BIG_GPU_MEMORY} GB memory" + )(test_case) + + +def require_big_accelerator(test_case): + """ + Decorator marking a test that requires a bigger hardware accelerator (24GB) for execution. Some example pipelines: + Flux, SD3, Cog, etc. + """ + import pytest + + test_case = pytest.mark.big_accelerator(test_case) + + if not is_torch_available(): + return pytest.mark.skip(reason="test requires PyTorch")(test_case) + + import torch + + if not (torch.cuda.is_available() or torch.xpu.is_available()): + return pytest.mark.skip(reason="test requires PyTorch CUDA")(test_case) + + if torch.xpu.is_available(): + device_properties = torch.xpu.get_device_properties(0) + else: + device_properties = torch.cuda.get_device_properties(0) + + total_memory = device_properties.total_memory / (1024**3) + return pytest.mark.skipif( + total_memory < BIG_GPU_MEMORY, + reason=f"test requires a hardware accelerator with at least {BIG_GPU_MEMORY} GB memory", + )(test_case) + + +def require_torch_accelerator_with_training(test_case): + """Decorator marking a test that requires an accelerator with support for training.""" + return pytest.mark.skipif( + not (is_torch_available() and backend_supports_training(torch_device)), + reason="test requires accelerator with training support", + )(test_case) + + +def skip_mps(test_case): + """Decorator marking a test to skip if torch_device is 'mps'""" + return pytest.mark.skipif(torch_device == "mps", reason="test requires non 'mps' device")(test_case) + + +def require_flax(test_case): + """ + Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed + """ + return pytest.mark.skipif(not is_flax_available(), reason="test requires JAX & Flax")(test_case) + + +def require_compel(test_case): + """ + Decorator marking a test that requires compel: https://github.com/damian0815/compel. These tests are skipped when + the library is not installed. + """ + return pytest.mark.skipif(not is_compel_available(), reason="test requires compel")(test_case) + + +def require_onnxruntime(test_case): + """ + Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed. + """ + return pytest.mark.skipif(not is_onnx_available(), reason="test requires onnxruntime")(test_case) + + +def require_note_seq(test_case): + """ + Decorator marking a test that requires note_seq. These tests are skipped when note_seq isn't installed. + """ + return pytest.mark.skipif(not is_note_seq_available(), reason="test requires note_seq")(test_case) + + +def require_accelerator(test_case): + """ + Decorator marking a test that requires a hardware accelerator backend. These tests are skipped when there are no + hardware accelerator available. + """ + return pytest.mark.skipif(torch_device == "cpu", reason="test requires a hardware accelerator")(test_case) + + +def require_torchsde(test_case): + """ + Decorator marking a test that requires torchsde. These tests are skipped when torchsde isn't installed. + """ + return pytest.mark.skipif(not is_torchsde_available(), reason="test requires torchsde")(test_case) + + +def require_peft_backend(test_case): + """ + Decorator marking a test that requires PEFT backend, this would require some specific versions of PEFT and + transformers. + """ + return pytest.mark.skipif(not USE_PEFT_BACKEND, reason="test requires PEFT backend")(test_case) + + +def require_timm(test_case): + """ + Decorator marking a test that requires timm. These tests are skipped when timm isn't installed. + """ + return pytest.mark.skipif(not is_timm_available(), reason="test requires timm")(test_case) + + +def require_bitsandbytes(test_case): + """ + Decorator marking a test that requires bitsandbytes. These tests are skipped when bitsandbytes isn't installed. + """ + return pytest.mark.skipif(not is_bitsandbytes_available(), reason="test requires bitsandbytes")(test_case) + + +def require_quanto(test_case): + """ + Decorator marking a test that requires quanto. These tests are skipped when quanto isn't installed. + """ + return pytest.mark.skipif(not is_optimum_quanto_available(), reason="test requires quanto")(test_case) + + +def require_accelerate(test_case): + """ + Decorator marking a test that requires accelerate. These tests are skipped when accelerate isn't installed. + """ + return pytest.mark.skipif(not is_accelerate_available(), reason="test requires accelerate")(test_case) + + +def require_peft_version_greater(peft_version): + """ + Decorator marking a test that requires PEFT backend with a specific version, this would require some specific + versions of PEFT and transformers. + """ + + def decorator(test_case): + correct_peft_version = is_peft_available() and version.parse( + version.parse(importlib.metadata.version("peft")).base_version + ) > version.parse(peft_version) + return pytest.mark.skipif( + not correct_peft_version, reason=f"test requires PEFT backend with the version greater than {peft_version}" + )(test_case) + + return decorator + + +def require_transformers_version_greater(transformers_version): + """ + Decorator marking a test that requires transformers with a specific version, this would require some specific + versions of PEFT and transformers. + """ + + def decorator(test_case): + correct_transformers_version = is_transformers_available() and version.parse( + version.parse(importlib.metadata.version("transformers")).base_version + ) > version.parse(transformers_version) + return pytest.mark.skipif( + not correct_transformers_version, + reason=f"test requires transformers with the version greater than {transformers_version}", + )(test_case) + + return decorator + + +def require_accelerate_version_greater(accelerate_version): + def decorator(test_case): + correct_accelerate_version = is_accelerate_available() and version.parse( + version.parse(importlib.metadata.version("accelerate")).base_version + ) > version.parse(accelerate_version) + return pytest.mark.skipif( + not correct_accelerate_version, + reason=f"Test requires accelerate with the version greater than {accelerate_version}.", + )(test_case) + + return decorator + + +def require_bitsandbytes_version_greater(bnb_version): + def decorator(test_case): + correct_bnb_version = is_bitsandbytes_available() and version.parse( + version.parse(importlib.metadata.version("bitsandbytes")).base_version + ) > version.parse(bnb_version) + return pytest.mark.skipif( + not correct_bnb_version, reason=f"Test requires bitsandbytes with the version greater than {bnb_version}." + )(test_case) + + return decorator + + +def require_hf_hub_version_greater(hf_hub_version): + def decorator(test_case): + correct_hf_hub_version = version.parse( + version.parse(importlib.metadata.version("huggingface_hub")).base_version + ) > version.parse(hf_hub_version) + return pytest.mark.skipif( + not correct_hf_hub_version, + reason=f"Test requires huggingface_hub with the version greater than {hf_hub_version}.", + )(test_case) + + return decorator + + +def require_gguf_version_greater_or_equal(gguf_version): + def decorator(test_case): + correct_gguf_version = is_gguf_available() and version.parse( + version.parse(importlib.metadata.version("gguf")).base_version + ) >= version.parse(gguf_version) + return pytest.mark.skipif( + not correct_gguf_version, reason=f"Test requires gguf with the version greater than {gguf_version}." + )(test_case) + + return decorator + + +def require_torchao_version_greater_or_equal(torchao_version): + def decorator(test_case): + correct_torchao_version = is_torchao_available() and version.parse( + version.parse(importlib.metadata.version("torchao")).base_version + ) >= version.parse(torchao_version) + return pytest.mark.skipif( + not correct_torchao_version, reason=f"Test requires torchao with version greater than {torchao_version}." + )(test_case) + + return decorator + + +def require_kernels_version_greater_or_equal(kernels_version): + def decorator(test_case): + correct_kernels_version = is_kernels_available() and version.parse( + version.parse(importlib.metadata.version("kernels")).base_version + ) >= version.parse(kernels_version) + return pytest.mark.skipif( + not correct_kernels_version, reason=f"Test requires kernels with version greater than {kernels_version}." + )(test_case) + + return decorator + + +def require_modelopt_version_greater_or_equal(modelopt_version): + def decorator(test_case): + return pytest.mark.skipif( + not is_nvidia_modelopt_version(">=", modelopt_version), + reason=f"Test requires modelopt with version greater than {modelopt_version}.", + )(test_case) + + return decorator + + +def deprecate_after_peft_backend(test_case): + """ + Decorator marking a test that will be skipped after PEFT backend + """ + return pytest.mark.skipif(USE_PEFT_BACKEND, reason="test skipped in favor of PEFT backend")(test_case) + + +def get_python_version(): + sys_info = sys.version_info + major, minor = sys_info.major, sys_info.minor + return major, minor + + +def load_numpy(arry: str | np.ndarray, local_path: str | None = None) -> np.ndarray: + if isinstance(arry, str): + if local_path is not None: + # local_path can be passed to correct images of tests + return Path(local_path, arry.split("/")[-5], arry.split("/")[-2], arry.split("/")[-1]).as_posix() + elif arry.startswith("http://") or arry.startswith("https://"): + response = requests.get(arry, timeout=DIFFUSERS_REQUEST_TIMEOUT) + response.raise_for_status() + arry = np.load(BytesIO(response.content)) + elif os.path.isfile(arry): + arry = np.load(arry) + else: + raise ValueError( + f"Incorrect path or url, URLs must start with `http://` or `https://`, and {arry} is not a valid path" + ) + elif isinstance(arry, np.ndarray): + pass + else: + raise ValueError( + "Incorrect format used for numpy ndarray. Should be an url linking to an image, a local path, or a" + " ndarray." + ) + + return arry + + +def load_pt(url: str, map_location: str | None = None, weights_only: bool = True): + response = requests.get(url, timeout=DIFFUSERS_REQUEST_TIMEOUT) + response.raise_for_status() + arry = torch.load(BytesIO(response.content), map_location=map_location, weights_only=weights_only) + return arry + + +def load_image(image: str | PIL.Image.Image) -> PIL.Image.Image: + """ + Loads `image` to a PIL Image. + + Args: + image (`str` or `PIL.Image.Image`): + The image to convert to the PIL Image format. + Returns: + `PIL.Image.Image`: + A PIL Image. + """ + if isinstance(image, str): + if image.startswith("http://") or image.startswith("https://"): + image = PIL.Image.open(requests.get(image, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT).raw) + elif os.path.isfile(image): + image = PIL.Image.open(image) + else: + raise ValueError( + f"Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path" + ) + elif isinstance(image, PIL.Image.Image): + image = image + else: + raise ValueError( + "Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image." + ) + image = PIL.ImageOps.exif_transpose(image) + image = image.convert("RGB") + return image + + +def preprocess_image(image: PIL.Image, batch_size: int): + w, h = image.size + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 + image = image.resize((w, h), resample=PIL.Image.LANCZOS) + image = np.array(image).astype(np.float32) / 255.0 + image = np.vstack([image[None].transpose(0, 3, 1, 2)] * batch_size) + image = torch.from_numpy(image) + return 2.0 * image - 1.0 + + +def export_to_gif(image: list[PIL.Image.Image], output_gif_path: str = None) -> str: + if output_gif_path is None: + output_gif_path = tempfile.NamedTemporaryFile(suffix=".gif").name + + image[0].save( + output_gif_path, + save_all=True, + append_images=image[1:], + optimize=False, + duration=100, + loop=0, + ) + return output_gif_path + + +@contextmanager +def buffered_writer(raw_f): + f = io.BufferedWriter(raw_f) + yield f + f.flush() + + +def export_to_ply(mesh, output_ply_path: str = None): + """ + Write a PLY file for a mesh. + """ + if output_ply_path is None: + output_ply_path = tempfile.NamedTemporaryFile(suffix=".ply").name + + coords = mesh.verts.detach().cpu().numpy() + faces = mesh.faces.cpu().numpy() + rgb = np.stack([mesh.vertex_channels[x].detach().cpu().numpy() for x in "RGB"], axis=1) + + with buffered_writer(open(output_ply_path, "wb")) as f: + f.write(b"ply\n") + f.write(b"format binary_little_endian 1.0\n") + f.write(bytes(f"element vertex {len(coords)}\n", "ascii")) + f.write(b"property float x\n") + f.write(b"property float y\n") + f.write(b"property float z\n") + if rgb is not None: + f.write(b"property uchar red\n") + f.write(b"property uchar green\n") + f.write(b"property uchar blue\n") + if faces is not None: + f.write(bytes(f"element face {len(faces)}\n", "ascii")) + f.write(b"property list uchar int vertex_index\n") + f.write(b"end_header\n") + + if rgb is not None: + rgb = (rgb * 255.499).round().astype(int) + vertices = [ + (*coord, *rgb) + for coord, rgb in zip( + coords.tolist(), + rgb.tolist(), + ) + ] + format = struct.Struct("<3f3B") + for item in vertices: + f.write(format.pack(*item)) + else: + format = struct.Struct("<3f") + for vertex in coords.tolist(): + f.write(format.pack(*vertex)) + + if faces is not None: + format = struct.Struct(" str: + if is_opencv_available(): + import cv2 + else: + raise ImportError(BACKENDS_MAPPING["opencv"][1].format("export_to_video")) + if output_video_path is None: + output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name + + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + h, w, c = video_frames[0].shape + video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=8, frameSize=(w, h)) + for i in range(len(video_frames)): + img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR) + video_writer.write(img) + return output_video_path + + +def load_hf_numpy(path) -> np.ndarray: + base_url = "https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main" + + if not path.startswith("http://") and not path.startswith("https://"): + path = os.path.join(base_url, urllib.parse.quote(path)) + + return load_numpy(path) + + +# --- pytest conf functions --- # + +# to avoid multiple invocation from tests/conftest.py and examples/conftest.py - make sure it's called only once +pytest_opt_registered = {} + + +def pytest_addoption_shared(parser): + """ + This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there. + + It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest` + option. + + """ + option = "--make-reports" + if option not in pytest_opt_registered: + parser.addoption( + option, + action="store", + default=False, + help="generate report files. The value of this option is used as a prefix to report names", + ) + pytest_opt_registered[option] = 1 + + +def pytest_terminal_summary_main(tr, id): + """ + Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current + directory. The report files are prefixed with the test suite name. + + This function emulates --duration and -rA pytest arguments. + + This function is to be called from `conftest.py` via `pytest_terminal_summary` wrapper that has to be defined + there. + + Args: + - tr: `terminalreporter` passed from `conftest.py` + - id: unique id like `tests` or `examples` that will be incorporated into the final reports filenames - this is + needed as some jobs have multiple runs of pytest, so we can't have them overwrite each other. + + NB: this functions taps into a private _pytest API and while unlikely, it could break should + pytest do internal changes - also it calls default internal methods of terminalreporter which + can be hijacked by various `pytest-` plugins and interfere. + + """ + from _pytest.config import create_terminal_writer + + if not len(id): + id = "tests" + + config = tr.config + orig_writer = config.get_terminal_writer() + orig_tbstyle = config.option.tbstyle + orig_reportchars = tr.reportchars + + dir = "reports" + Path(dir).mkdir(parents=True, exist_ok=True) + report_files = { + k: f"{dir}/{id}_{k}.txt" + for k in [ + "durations", + "errors", + "failures_long", + "failures_short", + "failures_line", + "passes", + "stats", + "summary_short", + "warnings", + ] + } + + # custom durations report + # note: there is no need to call pytest --durations=XX to get this separate report + # adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/runner.py#L66 + dlist = [] + for replist in tr.stats.values(): + for rep in replist: + if hasattr(rep, "duration"): + dlist.append(rep) + if dlist: + dlist.sort(key=lambda x: x.duration, reverse=True) + with open(report_files["durations"], "w") as f: + durations_min = 0.05 # sec + f.write("slowest durations\n") + for i, rep in enumerate(dlist): + if rep.duration < durations_min: + f.write(f"{len(dlist) - i} durations < {durations_min} secs were omitted") + break + f.write(f"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\n") + + def summary_failures_short(tr): + # expecting that the reports were --tb=long (default) so we chop them off here to the last frame + reports = tr.getreports("failed") + if not reports: + return + tr.write_sep("=", "FAILURES SHORT STACK") + for rep in reports: + msg = tr._getfailureheadline(rep) + tr.write_sep("_", msg, red=True, bold=True) + # chop off the optional leading extra frames, leaving only the last one + longrepr = re.sub(r".*_ _ _ (_ ){10,}_ _ ", "", rep.longreprtext, 0, re.M | re.S) + tr._tw.line(longrepr) + # note: not printing out any rep.sections to keep the report short + + # use ready-made report funcs, we are just hijacking the filehandle to log to a dedicated file each + # adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/terminal.py#L814 + # note: some pytest plugins may interfere by hijacking the default `terminalreporter` (e.g. + # pytest-instafail does that) + + # report failures with line/short/long styles + config.option.tbstyle = "auto" # full tb + with open(report_files["failures_long"], "w") as f: + tr._tw = create_terminal_writer(config, f) + tr.summary_failures() + + # config.option.tbstyle = "short" # short tb + with open(report_files["failures_short"], "w") as f: + tr._tw = create_terminal_writer(config, f) + summary_failures_short(tr) + + config.option.tbstyle = "line" # one line per error + with open(report_files["failures_line"], "w") as f: + tr._tw = create_terminal_writer(config, f) + tr.summary_failures() + + with open(report_files["errors"], "w") as f: + tr._tw = create_terminal_writer(config, f) + tr.summary_errors() + + with open(report_files["warnings"], "w") as f: + tr._tw = create_terminal_writer(config, f) + tr.summary_warnings() # normal warnings + tr.summary_warnings() # final warnings + + tr.reportchars = "wPpsxXEf" # emulate -rA (used in summary_passes() and short_test_summary()) + with open(report_files["passes"], "w") as f: + tr._tw = create_terminal_writer(config, f) + tr.summary_passes() + + with open(report_files["summary_short"], "w") as f: + tr._tw = create_terminal_writer(config, f) + tr.short_test_summary() + + with open(report_files["stats"], "w") as f: + tr._tw = create_terminal_writer(config, f) + tr.summary_stats() + + # restore: + tr._tw = orig_writer + tr.reportchars = orig_reportchars + config.option.tbstyle = orig_tbstyle + + +# Adapted from https://github.com/huggingface/transformers/blob/000e52aec8850d3fe2f360adc6fd256e5b47fe4c/src/transformers..testing_utils.py#L1905 +def is_flaky(max_attempts: int = 5, wait_before_retry: float | None = None, description: str | None = None): + """ + To decorate flaky tests (methods or entire classes). They will be retried on failures. + + Args: + max_attempts (`int`, *optional*, defaults to 5): + The maximum number of attempts to retry the flaky test. + wait_before_retry (`float`, *optional*): + If provided, will wait that number of seconds before retrying the test. + description (`str`, *optional*): + A string to describe the situation (what / where / why is flaky, link to GH issue/PR comments, errors, + etc.) + """ + + def decorator(obj): + # If decorating a class, wrap each test method on it + if inspect.isclass(obj): + for attr_name, attr_value in list(obj.__dict__.items()): + if callable(attr_value) and attr_name.startswith("test"): + # recursively decorate the method + setattr(obj, attr_name, decorator(attr_value)) + return obj + + # Otherwise we're decorating a single test function / method + @functools.wraps(obj) + def wrapper(*args, **kwargs): + retry_count = 1 + while retry_count < max_attempts: + try: + return obj(*args, **kwargs) + except Exception as err: + msg = ( + f"[FLAKY] {description or obj.__name__!r} " + f"failed on attempt {retry_count}/{max_attempts}: {err}" + ) + print(msg, file=sys.stderr) + if wait_before_retry is not None: + time.sleep(wait_before_retry) + retry_count += 1 + + return obj(*args, **kwargs) + + return wrapper + + return decorator + + +# Taken from: https://github.com/huggingface/transformers/blob/3658488ff77ff8d45101293e749263acf437f4d5/src/transformers..testing_utils.py#L1787 +def run_test_in_subprocess(test_case, target_func, inputs=None, timeout=None): + """ + To run a test in a subprocess. In particular, this can avoid (GPU) memory issue. + + Args: + test_case: + The test case object that will run `target_func`. + target_func (`Callable`): + The function implementing the actual testing logic. + inputs (`dict`, *optional*, defaults to `None`): + The inputs that will be passed to `target_func` through an (input) queue. + timeout (`int`, *optional*, defaults to `None`): + The timeout (in seconds) that will be passed to the input and output queues. If not specified, the env. + variable `PYTEST_TIMEOUT` will be checked. If still `None`, its value will be set to `600`. + """ + if timeout is None: + timeout = int(os.environ.get("PYTEST_TIMEOUT", 600)) + + start_methohd = "spawn" + ctx = multiprocessing.get_context(start_methohd) + + input_queue = ctx.Queue(1) + output_queue = ctx.JoinableQueue(1) + + # We can't send test case objects to the child, otherwise we get issues regarding pickle. + input_queue.put(inputs, timeout=timeout) + + process = ctx.Process(target=target_func, args=(input_queue, output_queue, timeout)) + process.start() + # Kill the child process if we can't get outputs from it in time: otherwise, the hanging subprocess prevents + # the test to exit properly. + try: + results = output_queue.get(timeout=timeout) + output_queue.task_done() + except Exception as e: + process.terminate() + test_case.fail(e) + process.join(timeout=timeout) + + if results["error"] is not None: + test_case.fail(f"{results['error']}") + + +class CaptureLogger: + """ + Args: + Context manager to capture `logging` streams + logger: 'logging` logger object + Returns: + The captured output is available via `self.out` + Example: + ```python + >>> from diffusers import logging + >>> from diffusers..testing_utils import CaptureLogger + + >>> msg = "Testing 1, 2, 3" + >>> logging.set_verbosity_info() + >>> logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.py") + >>> with CaptureLogger(logger) as cl: + ... logger.info(msg) + >>> assert cl.out, msg + "\n" + ``` + """ + + def __init__(self, logger): + self.logger = logger + self.io = StringIO() + self.sh = logging.StreamHandler(self.io) + self.out = "" + + def __enter__(self): + self.logger.addHandler(self.sh) + return self + + def __exit__(self, *exc): + self.logger.removeHandler(self.sh) + self.out = self.io.getvalue() + + def __repr__(self): + return f"captured: {self.out}\n" + + +def enable_full_determinism(): + """ + Helper function for reproducible behavior during distributed training. See + - https://pytorch.org/docs/stable/notes/randomness.html for pytorch + """ + # Enable PyTorch deterministic mode. This potentially requires either the environment + # variable 'CUDA_LAUNCH_BLOCKING' or 'CUBLAS_WORKSPACE_CONFIG' to be set, + # depending on the CUDA version, so we set them both here + os.environ["CUDA_LAUNCH_BLOCKING"] = "1" + # Use larger workspace size for PyTorch 2.10+ to avoid CUBLAS_STATUS_NOT_INITIALIZED errors + # (catches 2.11 dev versions which report as >= 2.10) + if is_torch_version(">=", "2.10"): + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + else: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" + torch.use_deterministic_algorithms(True) + + # Enable CUDNN deterministic mode + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + torch.backends.cuda.matmul.allow_tf32 = False + + +def disable_full_determinism(): + os.environ["CUDA_LAUNCH_BLOCKING"] = "0" + os.environ["CUBLAS_WORKSPACE_CONFIG"] = "" + torch.use_deterministic_algorithms(False) + + +# Utils for custom and alternative accelerator devices +def _is_torch_fp16_available(device): + if not is_torch_available(): + return False + + import torch + + device = torch.device(device) + + try: + x = torch.zeros((2, 2), dtype=torch.float16).to(device) + _ = torch.mul(x, x) + return True + + except Exception as e: + if device.type == "cuda": + raise ValueError( + f"You have passed a device of type 'cuda' which should work with 'fp16', but 'cuda' does not seem to be correctly installed on your machine: {e}" + ) + + return False + + +def _is_torch_fp64_available(device): + if not is_torch_available(): + return False + + import torch + + device = torch.device(device) + + try: + x = torch.zeros((2, 2), dtype=torch.float64).to(device) + _ = torch.mul(x, x) + return True + + except Exception as e: + if device.type == "cuda": + raise ValueError( + f"You have passed a device of type 'cuda' which should work with 'fp64', but 'cuda' does not seem to be correctly installed on your machine: {e}" + ) + + return False + + +# Guard these lookups for when Torch is not used - alternative accelerator support is for PyTorch +if is_torch_available(): + # Behaviour flags + BACKEND_SUPPORTS_TRAINING = {"cuda": True, "xpu": True, "cpu": True, "mps": False, "default": True} + + # Function definitions + BACKEND_EMPTY_CACHE = { + "cuda": torch.cuda.empty_cache, + "xpu": torch.xpu.empty_cache, + "cpu": None, + "mps": torch.mps.empty_cache, + "default": None, + } + BACKEND_DEVICE_COUNT = { + "cuda": torch.cuda.device_count, + "xpu": torch.xpu.device_count, + "cpu": lambda: 0, + "mps": lambda: 0, + "default": 0, + } + BACKEND_MANUAL_SEED = { + "cuda": torch.cuda.manual_seed, + "xpu": torch.xpu.manual_seed, + "cpu": torch.manual_seed, + "mps": torch.mps.manual_seed, + "default": torch.manual_seed, + } + BACKEND_RESET_PEAK_MEMORY_STATS = { + "cuda": torch.cuda.reset_peak_memory_stats, + "xpu": getattr(torch.xpu, "reset_peak_memory_stats", None), + "cpu": None, + "mps": None, + "default": None, + } + BACKEND_RESET_MAX_MEMORY_ALLOCATED = { + "cuda": torch.cuda.reset_max_memory_allocated, + "xpu": getattr(torch.xpu, "reset_peak_memory_stats", None), + "cpu": None, + "mps": None, + "default": None, + } + BACKEND_MAX_MEMORY_ALLOCATED = { + "cuda": torch.cuda.max_memory_allocated, + "xpu": getattr(torch.xpu, "max_memory_allocated", None), + "cpu": 0, + "mps": 0, + "default": 0, + } + BACKEND_SYNCHRONIZE = { + "cuda": torch.cuda.synchronize, + "xpu": getattr(torch.xpu, "synchronize", None), + "cpu": None, + "mps": None, + "default": None, + } + + +# This dispatches a defined function according to the accelerator from the function definitions. +def _device_agnostic_dispatch(device: str, dispatch_table: dict[str, Callable], *args, **kwargs): + if device not in dispatch_table: + return dispatch_table["default"](*args, **kwargs) + + fn = dispatch_table[device] + + # Some device agnostic functions return values. Need to guard against 'None' instead at + # user level + if not callable(fn): + return fn + + return fn(*args, **kwargs) + + +# These are callables which automatically dispatch the function specific to the accelerator +def backend_manual_seed(device: str, seed: int): + return _device_agnostic_dispatch(device, BACKEND_MANUAL_SEED, seed) + + +def backend_synchronize(device: str): + return _device_agnostic_dispatch(device, BACKEND_SYNCHRONIZE) + + +def backend_empty_cache(device: str): + return _device_agnostic_dispatch(device, BACKEND_EMPTY_CACHE) + + +def backend_device_count(device: str): + return _device_agnostic_dispatch(device, BACKEND_DEVICE_COUNT) + + +def backend_reset_peak_memory_stats(device: str): + return _device_agnostic_dispatch(device, BACKEND_RESET_PEAK_MEMORY_STATS) + + +def backend_reset_max_memory_allocated(device: str): + return _device_agnostic_dispatch(device, BACKEND_RESET_MAX_MEMORY_ALLOCATED) + + +def backend_max_memory_allocated(device: str): + return _device_agnostic_dispatch(device, BACKEND_MAX_MEMORY_ALLOCATED) + + +# These are callables which return boolean behaviour flags and can be used to specify some +# device agnostic alternative where the feature is unsupported. +def backend_supports_training(device: str): + if not is_torch_available(): + return False + + if device not in BACKEND_SUPPORTS_TRAINING: + device = "default" + + return BACKEND_SUPPORTS_TRAINING[device] + + +# Guard for when Torch is not available +if is_torch_available(): + # Update device function dict mapping + def update_mapping_from_spec(device_fn_dict: dict[str, Callable], attribute_name: str): + try: + # Try to import the function directly + spec_fn = getattr(device_spec_module, attribute_name) + device_fn_dict[torch_device] = spec_fn + except AttributeError as e: + # If the function doesn't exist, and there is no default, throw an error + if "default" not in device_fn_dict: + raise AttributeError( + f"`{attribute_name}` not found in '{device_spec_path}' and no default fallback function found." + ) from e + + if "DIFFUSERS_TEST_DEVICE_SPEC" in os.environ: + device_spec_path = os.environ["DIFFUSERS_TEST_DEVICE_SPEC"] + if not Path(device_spec_path).is_file(): + raise ValueError(f"Specified path to device specification file is not found. Received {device_spec_path}") + + try: + import_name = device_spec_path[: device_spec_path.index(".py")] + except ValueError as e: + raise ValueError(f"Provided device spec file is not a Python file! Received {device_spec_path}") from e + + device_spec_module = importlib.import_module(import_name) + + try: + device_name = device_spec_module.DEVICE_NAME + except AttributeError: + raise AttributeError("Device spec file did not contain `DEVICE_NAME`") + + if "DIFFUSERS_TEST_DEVICE" in os.environ and torch_device != device_name: + msg = f"Mismatch between environment variable `DIFFUSERS_TEST_DEVICE` '{torch_device}' and device found in spec '{device_name}'\n" + msg += "Either unset `DIFFUSERS_TEST_DEVICE` or ensure it matches device spec name." + raise ValueError(msg) + + torch_device = device_name + + # Add one entry here for each `BACKEND_*` dictionary. + update_mapping_from_spec(BACKEND_MANUAL_SEED, "MANUAL_SEED_FN") + update_mapping_from_spec(BACKEND_EMPTY_CACHE, "EMPTY_CACHE_FN") + update_mapping_from_spec(BACKEND_DEVICE_COUNT, "DEVICE_COUNT_FN") + update_mapping_from_spec(BACKEND_SUPPORTS_TRAINING, "SUPPORTS_TRAINING") + update_mapping_from_spec(BACKEND_RESET_PEAK_MEMORY_STATS, "RESET_PEAK_MEMORY_STATS_FN") + update_mapping_from_spec(BACKEND_RESET_MAX_MEMORY_ALLOCATED, "RESET_MAX_MEMORY_ALLOCATED_FN") + update_mapping_from_spec(BACKEND_MAX_MEMORY_ALLOCATED, "MAX_MEMORY_ALLOCATED_FN") + + +# Modified from https://github.com/huggingface/transformers/blob/cdfb018d0300fef3b07d9220f3efe9c2a9974662/src/transformers..testing_utils.py#L3090 + +# Type definition of key used in `Expectations` class. +DeviceProperties = Tuple[Union[str, None], Union[int, None]] + + +@functools.lru_cache +def get_device_properties() -> DeviceProperties: + """ + Get environment device properties. + """ + if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM: + import torch + + major, _ = torch.cuda.get_device_capability() + if IS_ROCM_SYSTEM: + return ("rocm", major) + else: + return ("cuda", major) + elif IS_XPU_SYSTEM: + import torch + + # To get more info of the architecture meaning and bit allocation, refer to https://github.com/intel/llvm/blob/sycl/sycl/include/sycl/ext/oneapi/experimental/device_architecture.def + arch = torch.xpu.get_device_capability()["architecture"] + gen_mask = 0x000000FF00000000 + gen = (arch & gen_mask) >> 32 + return ("xpu", gen) + else: + return (torch_device, None) + + +if TYPE_CHECKING: + DevicePropertiesUserDict = UserDict[DeviceProperties, Any] +else: + DevicePropertiesUserDict = UserDict + +if is_torch_available(): + from diffusers.hooks._common import _GO_LC_SUPPORTED_PYTORCH_LAYERS + from diffusers.hooks.group_offloading import ( + _GROUP_ID_LAZY_LEAF, + _compute_group_hash, + _find_parent_module_in_module_dict, + _gather_buffers_with_no_group_offloading_parent, + _gather_parameters_with_no_group_offloading_parent, + ) + + def _get_expected_safetensors_files( + module: torch.nn.Module, + offload_to_disk_path: str, + offload_type: str, + num_blocks_per_group: int | None = None, + block_modules: list[str] | None = None, + module_prefix: str = "", + ) -> set[str]: + expected_files = set() + + def get_hashed_filename(group_id: str) -> str: + short_hash = _compute_group_hash(group_id) + return os.path.join(offload_to_disk_path, f"group_{short_hash}.safetensors") + + if offload_type == "block_level": + if num_blocks_per_group is None: + raise ValueError("num_blocks_per_group must be provided for 'block_level' offloading.") + + block_modules_set = set(block_modules) if block_modules is not None else set() + + modules_with_group_offloading = set() + unmatched_modules = [] + for name, submodule in module.named_children(): + if name in block_modules_set: + new_prefix = f"{module_prefix}{name}." if module_prefix else f"{name}." + submodule_files = _get_expected_safetensors_files( + submodule, offload_to_disk_path, offload_type, num_blocks_per_group, block_modules, new_prefix + ) + expected_files.update(submodule_files) + modules_with_group_offloading.add(name) + + elif isinstance(submodule, (torch.nn.ModuleList, torch.nn.Sequential)): + for i in range(0, len(submodule), num_blocks_per_group): + current_modules = submodule[i : i + num_blocks_per_group] + if not current_modules: + continue + group_id = f"{module_prefix}{name}_{i}_{i + len(current_modules) - 1}" + expected_files.add(get_hashed_filename(group_id)) + for j in range(i, i + len(current_modules)): + modules_with_group_offloading.add(f"{name}.{j}") + else: + unmatched_modules.append(submodule) + + parameters = _gather_parameters_with_no_group_offloading_parent(module, modules_with_group_offloading) + buffers = _gather_buffers_with_no_group_offloading_parent(module, modules_with_group_offloading) + + if len(unmatched_modules) > 0 or len(parameters) > 0 or len(buffers) > 0: + expected_files.add(get_hashed_filename(f"{module_prefix}{module.__class__.__name__}_unmatched_group")) + + elif offload_type == "leaf_level": + # Handle leaf-level module groups + for name, submodule in module.named_modules(): + if isinstance(submodule, _GO_LC_SUPPORTED_PYTORCH_LAYERS): + # These groups will always have parameters, so a file is expected + expected_files.add(get_hashed_filename(name)) + + # Handle groups for non-leaf parameters/buffers + modules_with_group_offloading = { + name for name, sm in module.named_modules() if isinstance(sm, _GO_LC_SUPPORTED_PYTORCH_LAYERS) + } + parameters = _gather_parameters_with_no_group_offloading_parent(module, modules_with_group_offloading) + buffers = _gather_buffers_with_no_group_offloading_parent(module, modules_with_group_offloading) + + all_orphans = parameters + buffers + if all_orphans: + parent_to_tensors = {} + module_dict = dict(module.named_modules()) + for tensor_name, _ in all_orphans: + parent_name = _find_parent_module_in_module_dict(tensor_name, module_dict) + if parent_name not in parent_to_tensors: + parent_to_tensors[parent_name] = [] + parent_to_tensors[parent_name].append(tensor_name) + + for parent_name in parent_to_tensors: + # A file is expected for each parent that gathers orphaned tensors + expected_files.add(get_hashed_filename(parent_name)) + expected_files.add(get_hashed_filename(_GROUP_ID_LAZY_LEAF)) + + else: + raise ValueError(f"Unsupported offload_type: {offload_type}") + + return expected_files + + def _check_safetensors_serialization( + module: torch.nn.Module, + offload_to_disk_path: str, + offload_type: str, + num_blocks_per_group: int | None = None, + block_modules: list[str] | None = None, + ) -> bool: + if not os.path.isdir(offload_to_disk_path): + return False, None, None + + expected_files = _get_expected_safetensors_files( + module, offload_to_disk_path, offload_type, num_blocks_per_group, block_modules + ) + actual_files = set(glob.glob(os.path.join(offload_to_disk_path, "*.safetensors"))) + missing_files = expected_files - actual_files + extra_files = actual_files - expected_files + + is_correct = not missing_files and not extra_files + return is_correct, extra_files, missing_files + + +class Expectations(DevicePropertiesUserDict): + def get_expectation(self) -> Any: + """ + Find best matching expectation based on environment device properties. + """ + return self.find_expectation(get_device_properties()) + + @staticmethod + def is_default(key: DeviceProperties) -> bool: + return all(p is None for p in key) + + @staticmethod + def score(key: DeviceProperties, other: DeviceProperties) -> int: + """ + Returns score indicating how similar two instances of the `Properties` tuple are. Points are calculated using + bits, but documented as int. Rules are as follows: + * Matching `type` gives 8 points. + * Semi-matching `type`, for example cuda and rocm, gives 4 points. + * Matching `major` (compute capability major version) gives 2 points. + * Default expectation (if present) gives 1 points. + """ + (device_type, major) = key + (other_device_type, other_major) = other + + score = 0b0 + if device_type == other_device_type: + score |= 0b1000 + elif device_type in ["cuda", "rocm"] and other_device_type in ["cuda", "rocm"]: + score |= 0b100 + + if major == other_major and other_major is not None: + score |= 0b10 + + if Expectations.is_default(other): + score |= 0b1 + + return int(score) + + def find_expectation(self, key: DeviceProperties = (None, None)) -> Any: + """ + Find best matching expectation based on provided device properties. + """ + (result_key, result) = max(self.data.items(), key=lambda x: Expectations.score(key, x[0])) + + if Expectations.score(key, result_key) == 0: + raise ValueError(f"No matching expectation found for {key}") + + return result + + def __repr__(self): + return f"{self.data}" diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_config_docstrings.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_config_docstrings.py new file mode 100644 index 0000000000000000000000000000000000000000..d39fe6a618d4d7e0476f32578a488aa3402abde6 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_config_docstrings.py @@ -0,0 +1,84 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import importlib +import inspect +import os +import re + + +# All paths are set with the intent you should run this script from the root of the repo with the command +# python utils/check_config_docstrings.py +PATH_TO_TRANSFORMERS = "src/transformers" + + +# This is to make sure the transformers module imported is the one in the repo. +spec = importlib.util.spec_from_file_location( + "transformers", + os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), + submodule_search_locations=[PATH_TO_TRANSFORMERS], +) +transformers = spec.loader.load_module() + +CONFIG_MAPPING = transformers.models.auto.configuration_auto.CONFIG_MAPPING + +# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`. +# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)` +_re_checkpoint = re.compile(r"\[(.+?)\]\((https://huggingface\.co/.+?)\)") + + +CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = { + "CLIPConfigMixin", + "DecisionTransformerConfigMixin", + "EncoderDecoderConfigMixin", + "RagConfigMixin", + "SpeechEncoderDecoderConfigMixin", + "VisionEncoderDecoderConfigMixin", + "VisionTextDualEncoderConfigMixin", +} + + +def check_config_docstrings_have_checkpoints(): + configs_without_checkpoint = [] + + for config_class in list(CONFIG_MAPPING.values()): + checkpoint_found = False + + # source code of `config_class` + config_source = inspect.getsource(config_class) + checkpoints = _re_checkpoint.findall(config_source) + + for checkpoint in checkpoints: + # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link. + # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')` + ckpt_name, ckpt_link = checkpoint + + # verify the checkpoint name corresponds to the checkpoint link + ckpt_link_from_name = f"https://huggingface.co/{ckpt_name}" + if ckpt_link == ckpt_link_from_name: + checkpoint_found = True + break + + name = config_class.__name__ + if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK: + configs_without_checkpoint.append(name) + + if len(configs_without_checkpoint) > 0: + message = "\n".join(sorted(configs_without_checkpoint)) + raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}") + + +if __name__ == "__main__": + check_config_docstrings_have_checkpoints() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_copies.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_copies.py new file mode 100644 index 0000000000000000000000000000000000000000..001366c1905f5fda8a8e22f38b532013657e8ee3 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_copies.py @@ -0,0 +1,222 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import glob +import os +import re +import subprocess + + +# All paths are set with the intent you should run this script from the root of the repo with the command +# python utils/check_copies.py +DIFFUSERS_PATH = "src/diffusers" +REPO_PATH = "." + + +def _should_continue(line, indent): + return line.startswith(indent) or len(line) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$", line) is not None + + +def find_code_in_diffusers(object_name): + """Find and return the code source code of `object_name`.""" + parts = object_name.split(".") + i = 0 + + # First let's find the module where our object lives. + module = parts[i] + while i < len(parts) and not os.path.isfile(os.path.join(DIFFUSERS_PATH, f"{module}.py")): + i += 1 + if i < len(parts): + module = os.path.join(module, parts[i]) + if i >= len(parts): + raise ValueError(f"`object_name` should begin with the name of a module of diffusers but got {object_name}.") + + with open( + os.path.join(DIFFUSERS_PATH, f"{module}.py"), + "r", + encoding="utf-8", + newline="\n", + ) as f: + lines = f.readlines() + + # Now let's find the class / func in the code! + indent = "" + line_index = 0 + for name in parts[i + 1 :]: + while ( + line_index < len(lines) and re.search(rf"^{indent}(class|def)\s+{name}(\(|\:)", lines[line_index]) is None + ): + line_index += 1 + indent += " " + line_index += 1 + + if line_index >= len(lines): + raise ValueError(f" {object_name} does not match any function or class in {module}.") + + # We found the beginning of the class / func, now let's find the end (when the indent diminishes). + start_index = line_index + while line_index < len(lines) and _should_continue(lines[line_index], indent): + line_index += 1 + # Clean up empty lines at the end (if any). + while len(lines[line_index - 1]) <= 1: + line_index -= 1 + + code_lines = lines[start_index:line_index] + return "".join(code_lines) + + +_re_copy_warning = re.compile(r"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)") +_re_replace_pattern = re.compile(r"^\s*(\S+)->(\S+)(\s+.*|$)") +_re_fill_pattern = re.compile(r"]*>") + + +def get_indent(code): + lines = code.split("\n") + idx = 0 + while idx < len(lines) and len(lines[idx]) == 0: + idx += 1 + if idx < len(lines): + return re.search(r"^(\s*)\S", lines[idx]).groups()[0] + return "" + + +def run_ruff(code): + command = ["ruff", "format", "-", "--config", "pyproject.toml", "--silent"] + process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) + stdout, _ = process.communicate(input=code.encode()) + return stdout.decode() + + +def stylify(code: str) -> str: + """ + Applies the ruff part of our `make style` command to some code. This formats the code using `ruff format`. + As `ruff` does not provide a python api this cannot be done on the fly. + + Args: + code (`str`): The code to format. + + Returns: + `str`: The formatted code. + """ + has_indent = len(get_indent(code)) > 0 + if has_indent: + code = f"class Bla:\n{code}" + formatted_code = run_ruff(code) + return formatted_code[len("class Bla:\n") :] if has_indent else formatted_code + + +def is_copy_consistent(filename, overwrite=False): + """ + Check if the code commented as a copy in `filename` matches the original. + Return the differences or overwrites the content depending on `overwrite`. + """ + with open(filename, "r", encoding="utf-8", newline="\n") as f: + lines = f.readlines() + diffs = [] + line_index = 0 + # Not a for loop cause `lines` is going to change (if `overwrite=True`). + while line_index < len(lines): + search = _re_copy_warning.search(lines[line_index]) + if search is None: + line_index += 1 + continue + + # There is some copied code here, let's retrieve the original. + indent, object_name, replace_pattern = search.groups() + theoretical_code = find_code_in_diffusers(object_name) + theoretical_indent = get_indent(theoretical_code) + + start_index = line_index + 1 if indent == theoretical_indent else line_index + 2 + indent = theoretical_indent + line_index = start_index + + # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment. + should_continue = True + while line_index < len(lines) and should_continue: + line_index += 1 + if line_index >= len(lines): + break + line = lines[line_index] + should_continue = _should_continue(line, indent) and re.search(f"^{indent}# End copy", line) is None + # Clean up empty lines at the end (if any). + while len(lines[line_index - 1]) <= 1: + line_index -= 1 + + observed_code_lines = lines[start_index:line_index] + observed_code = "".join(observed_code_lines) + + # Remove any nested `Copied from` comments to avoid circular copies + theoretical_code = [line for line in theoretical_code.split("\n") if _re_copy_warning.search(line) is None] + theoretical_code = "\n".join(theoretical_code) + + # Before comparing, use the `replace_pattern` on the original code. + if len(replace_pattern) > 0: + patterns = replace_pattern.replace("with", "").split(",") + patterns = [_re_replace_pattern.search(p) for p in patterns] + for pattern in patterns: + if pattern is None: + continue + obj1, obj2, option = pattern.groups() + theoretical_code = re.sub(obj1, obj2, theoretical_code) + if option.strip() == "all-casing": + theoretical_code = re.sub(obj1.lower(), obj2.lower(), theoretical_code) + theoretical_code = re.sub(obj1.upper(), obj2.upper(), theoretical_code) + + # stylify after replacement. To be able to do that, we need the header (class or function definition) + # from the previous line + theoretical_code = stylify(lines[start_index - 1] + theoretical_code) + theoretical_code = theoretical_code[len(lines[start_index - 1]) :] + + # Test for a diff and act accordingly. + if observed_code != theoretical_code: + diffs.append([object_name, start_index]) + if overwrite: + lines = lines[:start_index] + [theoretical_code] + lines[line_index:] + line_index = start_index + 1 + + if overwrite and len(diffs) > 0: + # Warn the user a file has been modified. + print(f"Detected changes, rewriting {filename}.") + with open(filename, "w", encoding="utf-8", newline="\n") as f: + f.writelines(lines) + return diffs + + +def check_copies(overwrite: bool = False): + all_files = glob.glob(os.path.join(DIFFUSERS_PATH, "**/*.py"), recursive=True) + diffs = [] + for filename in all_files: + new_diffs = is_copy_consistent(filename, overwrite) + diffs += [f"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs] + if not overwrite and len(diffs) > 0: + diff = "\n".join(diffs) + raise Exception( + "Found the following copy inconsistencies:\n" + + diff + + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them." + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--fix_and_overwrite", + action="store_true", + help="Whether to fix inconsistencies.", + ) + args = parser.parse_args() + + check_copies(args.fix_and_overwrite) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_doc_toc.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_doc_toc.py new file mode 100644 index 0000000000000000000000000000000000000000..050b093991e6c32437b2d4de7f58004de3012082 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_doc_toc.py @@ -0,0 +1,212 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +from collections import defaultdict + +import yaml + + +PATH_TO_TOC = "docs/source/en/_toctree.yml" + +# Titles that should maintain their position and not be sorted alphabetically +FIXED_POSITION_TITLES = {"overview", "autopipeline"} + + +def clean_doc_toc(doc_list): + """ + Cleans the table of content of the model documentation by removing duplicates and sorting models alphabetically. + """ + counts = defaultdict(int) + fixed_position_docs = [] + new_doc_list = [] + for doc in doc_list: + if "local" in doc: + counts[doc["local"]] += 1 + + if doc["title"].lower() in FIXED_POSITION_TITLES: + fixed_position_docs.append({"local": doc["local"], "title": doc["title"]}) + else: + new_doc_list.append(doc) + + doc_list = new_doc_list + duplicates = [key for key, value in counts.items() if value > 1] + + new_doc = [] + for duplicate_key in duplicates: + titles = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}) + if len(titles) > 1: + raise ValueError( + f"{duplicate_key} is present several times in the documentation table of content at " + "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the " + "others." + ) + # Only add this once + new_doc.append({"local": duplicate_key, "title": titles[0]}) + + # Add none duplicate-keys + new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1]) + new_doc = sorted(new_doc, key=lambda s: s["title"].lower()) + + # Fixed-position titles maintain their original order + result = [] + for doc in fixed_position_docs: + result.append(doc) + + result.extend(new_doc) + return result + + +def check_scheduler_doc(overwrite=False): + with open(PATH_TO_TOC, encoding="utf-8") as f: + content = yaml.safe_load(f.read()) + + # Get to the API doc + api_idx = 0 + while content[api_idx]["title"] != "API": + api_idx += 1 + api_doc = content[api_idx]["sections"] + + # Then to the model doc + scheduler_idx = 0 + while api_doc[scheduler_idx]["title"] != "Schedulers": + scheduler_idx += 1 + + scheduler_doc = api_doc[scheduler_idx]["sections"] + new_scheduler_doc = clean_doc_toc(scheduler_doc) + + diff = False + if new_scheduler_doc != scheduler_doc: + diff = True + if overwrite: + api_doc[scheduler_idx]["sections"] = new_scheduler_doc + + if diff: + if overwrite: + content[api_idx]["sections"] = api_doc + with open(PATH_TO_TOC, "w", encoding="utf-8") as f: + f.write(yaml.dump(content, allow_unicode=True)) + else: + raise ValueError( + "The model doc part of the table of content is not properly sorted, run `make style` to fix this." + ) + + +def check_pipeline_doc(overwrite=False): + with open(PATH_TO_TOC, encoding="utf-8") as f: + content = yaml.safe_load(f.read()) + + # Get to the API doc + api_idx = 0 + while content[api_idx]["title"] != "API": + api_idx += 1 + api_doc = content[api_idx]["sections"] + + # Then to the model doc + pipeline_idx = 0 + while api_doc[pipeline_idx]["title"] != "Pipelines": + pipeline_idx += 1 + + diff = False + pipeline_docs = api_doc[pipeline_idx]["sections"] + new_pipeline_docs = [] + + # sort sub pipeline docs + for pipeline_doc in pipeline_docs: + if "sections" in pipeline_doc: + sub_pipeline_doc = pipeline_doc["sections"] + new_sub_pipeline_doc = clean_doc_toc(sub_pipeline_doc) + if new_sub_pipeline_doc != sub_pipeline_doc: + diff = True + if overwrite: + pipeline_doc["sections"] = new_sub_pipeline_doc + new_pipeline_docs.append(pipeline_doc) + + # sort overall pipeline doc + new_pipeline_docs = clean_doc_toc(new_pipeline_docs) + + if new_pipeline_docs != pipeline_docs: + diff = True + if overwrite: + api_doc[pipeline_idx]["sections"] = new_pipeline_docs + + if diff: + if overwrite: + content[api_idx]["sections"] = api_doc + with open(PATH_TO_TOC, "w", encoding="utf-8") as f: + f.write(yaml.dump(content, allow_unicode=True)) + else: + raise ValueError( + "The model doc part of the table of content is not properly sorted, run `make style` to fix this." + ) + + +def check_model_doc(overwrite=False): + with open(PATH_TO_TOC, encoding="utf-8") as f: + content = yaml.safe_load(f.read()) + + # Get to the API doc + api_idx = 0 + while content[api_idx]["title"] != "API": + api_idx += 1 + api_doc = content[api_idx]["sections"] + + # Then to the model doc + model_idx = 0 + while api_doc[model_idx]["title"] != "Models": + model_idx += 1 + + diff = False + model_docs = api_doc[model_idx]["sections"] + new_model_docs = [] + + # sort sub model docs + for model_doc in model_docs: + if "sections" in model_doc: + sub_model_doc = model_doc["sections"] + new_sub_model_doc = clean_doc_toc(sub_model_doc) + if new_sub_model_doc != sub_model_doc: + diff = True + if overwrite: + model_doc["sections"] = new_sub_model_doc + new_model_docs.append(model_doc) + + # sort overall model doc + new_model_docs = clean_doc_toc(new_model_docs) + + if new_model_docs != model_docs: + diff = True + if overwrite: + api_doc[model_idx]["sections"] = new_model_docs + + if diff: + if overwrite: + content[api_idx]["sections"] = api_doc + with open(PATH_TO_TOC, "w", encoding="utf-8") as f: + f.write(yaml.dump(content, allow_unicode=True)) + else: + raise ValueError( + "The model doc part of the table of content is not properly sorted, run `make style` to fix this." + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") + args = parser.parse_args() + + check_scheduler_doc(args.fix_and_overwrite) + check_pipeline_doc(args.fix_and_overwrite) + check_model_doc(args.fix_and_overwrite) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_dummies.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_dummies.py new file mode 100644 index 0000000000000000000000000000000000000000..04a670c2f5d91c76b32b2c6d200b342675df6bb0 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_dummies.py @@ -0,0 +1,175 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import re + + +# All paths are set with the intent you should run this script from the root of the repo with the command +# python utils/check_dummies.py +PATH_TO_DIFFUSERS = "src/diffusers" + +# Matches is_xxx_available() +_re_backend = re.compile(r"is\_([a-z_]*)_available\(\)") +# Matches from xxx import bla +_re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n") + + +DUMMY_CONSTANT = """ +{0} = None +""" + +DUMMY_CLASS = """ +class {0}(metaclass=DummyObject): + _backends = {1} + + def __init__(self, *args, **kwargs): + requires_backends(self, {1}) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, {1}) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, {1}) +""" + + +DUMMY_FUNCTION = """ +def {0}(*args, **kwargs): + requires_backends({0}, {1}) +""" + + +def find_backend(line): + """Find one (or multiple) backend in a code line of the init.""" + backends = _re_backend.findall(line) + if len(backends) == 0: + return None + + return "_and_".join(backends) + + +def read_init(): + """Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects.""" + with open(os.path.join(PATH_TO_DIFFUSERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f: + lines = f.readlines() + + # Get to the point we do the actual imports for type checking + line_index = 0 + while not lines[line_index].startswith("if TYPE_CHECKING"): + line_index += 1 + + backend_specific_objects = {} + # Go through the end of the file + while line_index < len(lines): + # If the line contains is_backend_available, we grab all objects associated with the `else` block + backend = find_backend(lines[line_index]) + if backend is not None: + while not lines[line_index].startswith(" else:"): + line_index += 1 + line_index += 1 + objects = [] + # Until we unindent, add backend objects to the list + while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8): + line = lines[line_index] + single_line_import_search = _re_single_line_import.search(line) + if single_line_import_search is not None: + objects.extend(single_line_import_search.groups()[0].split(", ")) + elif line.startswith(" " * 12): + objects.append(line[12:-2]) + line_index += 1 + + if len(objects) > 0: + backend_specific_objects[backend] = objects + else: + line_index += 1 + + return backend_specific_objects + + +def create_dummy_object(name, backend_name): + """Create the code for the dummy object corresponding to `name`.""" + if name.isupper(): + return DUMMY_CONSTANT.format(name) + elif name.islower(): + return DUMMY_FUNCTION.format(name, backend_name) + else: + return DUMMY_CLASS.format(name, backend_name) + + +def create_dummy_files(backend_specific_objects=None): + """Create the content of the dummy files.""" + if backend_specific_objects is None: + backend_specific_objects = read_init() + # For special correspondence backend to module name as used in the function requires_modulename + dummy_files = {} + + for backend, objects in backend_specific_objects.items(): + backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]" + dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n" + dummy_file += "from ..utils import DummyObject, requires_backends\n\n" + dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects]) + dummy_files[backend] = dummy_file + + return dummy_files + + +def check_dummies(overwrite=False): + """Check if the dummy files are up to date and maybe `overwrite` with the right content.""" + dummy_files = create_dummy_files() + # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py + short_names = {"torch": "pt"} + + # Locate actual dummy modules and read their content. + path = os.path.join(PATH_TO_DIFFUSERS, "utils") + dummy_file_paths = { + backend: os.path.join(path, f"dummy_{short_names.get(backend, backend)}_objects.py") + for backend in dummy_files.keys() + } + + actual_dummies = {} + for backend, file_path in dummy_file_paths.items(): + if os.path.isfile(file_path): + with open(file_path, "r", encoding="utf-8", newline="\n") as f: + actual_dummies[backend] = f.read() + else: + actual_dummies[backend] = "" + + for backend in dummy_files.keys(): + if dummy_files[backend] != actual_dummies[backend]: + if overwrite: + print( + f"Updating diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main " + "__init__ has new objects." + ) + with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n") as f: + f.write(dummy_files[backend]) + else: + raise ValueError( + "The main __init__ has objects that are not present in " + f"diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` " + "to fix this." + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") + args = parser.parse_args() + + check_dummies(args.fix_and_overwrite) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_inits.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_inits.py new file mode 100644 index 0000000000000000000000000000000000000000..8208fa634186536767054e8fb5f0d6f81bee7ba0 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_inits.py @@ -0,0 +1,299 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections +import importlib.util +import os +import re +from pathlib import Path + + +PATH_TO_TRANSFORMERS = "src/transformers" + + +# Matches is_xxx_available() +_re_backend = re.compile(r"is\_([a-z_]*)_available()") +# Catches a one-line _import_struct = {xxx} +_re_one_line_import_struct = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}") +# Catches a line with a key-values pattern: "bla": ["foo", "bar"] +_re_import_struct_key_value = re.compile(r'\s+"\S*":\s+\[([^\]]*)\]') +# Catches a line if not is_foo_available +_re_test_backend = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)") +# Catches a line _import_struct["bla"].append("foo") +_re_import_struct_add_one = re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)') +# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"] +_re_import_struct_add_many = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]") +# Catches a line with an object between quotes and a comma: "MyModel", +_re_quote_object = re.compile(r'^\s+"([^"]+)",') +# Catches a line with objects between brackets only: ["foo", "bar"], +_re_between_brackets = re.compile(r"^\s+\[([^\]]+)\]") +# Catches a line with from foo import bar, bla, boo +_re_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n") +# Catches a line with try: +_re_try = re.compile(r"^\s*try:") +# Catches a line with else: +_re_else = re.compile(r"^\s*else:") + + +def find_backend(line): + """Find one (or multiple) backend in a code line of the init.""" + if _re_test_backend.search(line) is None: + return None + backends = [b[0] for b in _re_backend.findall(line)] + backends.sort() + return "_and_".join(backends) + + +def parse_init(init_file): + """ + Read an init_file and parse (per backend) the _import_structure objects defined and the TYPE_CHECKING objects + defined + """ + with open(init_file, "r", encoding="utf-8", newline="\n") as f: + lines = f.readlines() + + line_index = 0 + while line_index < len(lines) and not lines[line_index].startswith("_import_structure = {"): + line_index += 1 + + # If this is a traditional init, just return. + if line_index >= len(lines): + return None + + # First grab the objects without a specific backend in _import_structure + objects = [] + while not lines[line_index].startswith("if TYPE_CHECKING") and find_backend(lines[line_index]) is None: + line = lines[line_index] + # If we have everything on a single line, let's deal with it. + if _re_one_line_import_struct.search(line): + content = _re_one_line_import_struct.search(line).groups()[0] + imports = re.findall(r"\[([^\]]+)\]", content) + for imp in imports: + objects.extend([obj[1:-1] for obj in imp.split(", ")]) + line_index += 1 + continue + single_line_import_search = _re_import_struct_key_value.search(line) + if single_line_import_search is not None: + imports = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", ") if len(obj) > 0] + objects.extend(imports) + elif line.startswith(" " * 8 + '"'): + objects.append(line[9:-3]) + line_index += 1 + + import_dict_objects = {"none": objects} + # Let's continue with backend-specific objects in _import_structure + while not lines[line_index].startswith("if TYPE_CHECKING"): + # If the line is an if not is_backend_available, we grab all objects associated. + backend = find_backend(lines[line_index]) + # Check if the backend declaration is inside a try block: + if _re_try.search(lines[line_index - 1]) is None: + backend = None + + if backend is not None: + line_index += 1 + + # Scroll until we hit the else block of try-except-else + while _re_else.search(lines[line_index]) is None: + line_index += 1 + + line_index += 1 + + objects = [] + # Until we unindent, add backend objects to the list + while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 4): + line = lines[line_index] + if _re_import_struct_add_one.search(line) is not None: + objects.append(_re_import_struct_add_one.search(line).groups()[0]) + elif _re_import_struct_add_many.search(line) is not None: + imports = _re_import_struct_add_many.search(line).groups()[0].split(", ") + imports = [obj[1:-1] for obj in imports if len(obj) > 0] + objects.extend(imports) + elif _re_between_brackets.search(line) is not None: + imports = _re_between_brackets.search(line).groups()[0].split(", ") + imports = [obj[1:-1] for obj in imports if len(obj) > 0] + objects.extend(imports) + elif _re_quote_object.search(line) is not None: + objects.append(_re_quote_object.search(line).groups()[0]) + elif line.startswith(" " * 8 + '"'): + objects.append(line[9:-3]) + elif line.startswith(" " * 12 + '"'): + objects.append(line[13:-3]) + line_index += 1 + + import_dict_objects[backend] = objects + else: + line_index += 1 + + # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend + objects = [] + while ( + line_index < len(lines) + and find_backend(lines[line_index]) is None + and not lines[line_index].startswith("else") + ): + line = lines[line_index] + single_line_import_search = _re_import.search(line) + if single_line_import_search is not None: + objects.extend(single_line_import_search.groups()[0].split(", ")) + elif line.startswith(" " * 8): + objects.append(line[8:-2]) + line_index += 1 + + type_hint_objects = {"none": objects} + # Let's continue with backend-specific objects + while line_index < len(lines): + # If the line is an if is_backend_available, we grab all objects associated. + backend = find_backend(lines[line_index]) + # Check if the backend declaration is inside a try block: + if _re_try.search(lines[line_index - 1]) is None: + backend = None + + if backend is not None: + line_index += 1 + + # Scroll until we hit the else block of try-except-else + while _re_else.search(lines[line_index]) is None: + line_index += 1 + + line_index += 1 + + objects = [] + # Until we unindent, add backend objects to the list + while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8): + line = lines[line_index] + single_line_import_search = _re_import.search(line) + if single_line_import_search is not None: + objects.extend(single_line_import_search.groups()[0].split(", ")) + elif line.startswith(" " * 12): + objects.append(line[12:-2]) + line_index += 1 + + type_hint_objects[backend] = objects + else: + line_index += 1 + + return import_dict_objects, type_hint_objects + + +def analyze_results(import_dict_objects, type_hint_objects): + """ + Analyze the differences between _import_structure objects and TYPE_CHECKING objects found in an init. + """ + + def find_duplicates(seq): + return [k for k, v in collections.Counter(seq).items() if v > 1] + + if list(import_dict_objects.keys()) != list(type_hint_objects.keys()): + return ["Both sides of the init do not have the same backends!"] + + errors = [] + for key in import_dict_objects.keys(): + duplicate_imports = find_duplicates(import_dict_objects[key]) + if duplicate_imports: + errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}") + duplicate_type_hints = find_duplicates(type_hint_objects[key]) + if duplicate_type_hints: + errors.append(f"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}") + + if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])): + name = "base imports" if key == "none" else f"{key} backend" + errors.append(f"Differences for {name}:") + for a in type_hint_objects[key]: + if a not in import_dict_objects[key]: + errors.append(f" {a} in TYPE_HINT but not in _import_structure.") + for a in import_dict_objects[key]: + if a not in type_hint_objects[key]: + errors.append(f" {a} in _import_structure but not in TYPE_HINT.") + return errors + + +def check_all_inits(): + """ + Check all inits in the transformers repo and raise an error if at least one does not define the same objects in + both halves. + """ + failures = [] + for root, _, files in os.walk(PATH_TO_TRANSFORMERS): + if "__init__.py" in files: + fname = os.path.join(root, "__init__.py") + objects = parse_init(fname) + if objects is not None: + errors = analyze_results(*objects) + if len(errors) > 0: + errors[0] = f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}" + failures.append("\n".join(errors)) + if len(failures) > 0: + raise ValueError("\n\n".join(failures)) + + +def get_transformers_submodules(): + """ + Returns the list of Transformers submodules. + """ + submodules = [] + for path, directories, files in os.walk(PATH_TO_TRANSFORMERS): + for folder in directories: + # Ignore private modules + if folder.startswith("_"): + directories.remove(folder) + continue + # Ignore leftovers from branches (empty folders apart from pycache) + if len(list((Path(path) / folder).glob("*.py"))) == 0: + continue + short_path = str((Path(path) / folder).relative_to(PATH_TO_TRANSFORMERS)) + submodule = short_path.replace(os.path.sep, ".") + submodules.append(submodule) + for fname in files: + if fname == "__init__.py": + continue + short_path = str((Path(path) / fname).relative_to(PATH_TO_TRANSFORMERS)) + submodule = short_path.replace(".py", "").replace(os.path.sep, ".") + if len(submodule.split(".")) == 1: + submodules.append(submodule) + return submodules + + +IGNORE_SUBMODULES = [ + "convert_pytorch_checkpoint_to_tf2", + "modeling_flax_pytorch_utils", +] + + +def check_submodules(): + # This is to make sure the transformers module imported is the one in the repo. + spec = importlib.util.spec_from_file_location( + "transformers", + os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), + submodule_search_locations=[PATH_TO_TRANSFORMERS], + ) + transformers = spec.loader.load_module() + + module_not_registered = [ + module + for module in get_transformers_submodules() + if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys() + ] + if len(module_not_registered) > 0: + list_of_modules = "\n".join(f"- {module}" for module in module_not_registered) + raise ValueError( + "The following submodules are not properly registered in the main init of Transformers:\n" + f"{list_of_modules}\n" + "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value." + ) + + +if __name__ == "__main__": + check_all_inits() + check_submodules() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_repo.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_repo.py new file mode 100644 index 0000000000000000000000000000000000000000..14bdbe60adf06e4b6a3f79378f68393cc083b6a3 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_repo.py @@ -0,0 +1,755 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import importlib +import inspect +import os +import re +import warnings +from collections import OrderedDict +from difflib import get_close_matches +from pathlib import Path + +from diffusers.models.auto import get_values +from diffusers.utils import ENV_VARS_TRUE_VALUES, is_flax_available, is_torch_available + + +# All paths are set with the intent you should run this script from the root of the repo with the command +# python utils/check_repo.py +PATH_TO_DIFFUSERS = "src/diffusers" +PATH_TO_TESTS = "tests" +PATH_TO_DOC = "docs/source/en" + +# Update this list with models that are supposed to be private. +PRIVATE_MODELS = [ + "DPRSpanPredictor", + "RealmBertModel", + "T5Stack", + "TFDPRSpanPredictor", +] + +# Update this list for models that are not tested with a comment explaining the reason it should not be. +# Being in this list is an exception and should **not** be the rule. +IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [ + # models to ignore for not tested + "OPTDecoder", # Building part of bigger (tested) model. + "DecisionTransformerGPT2Model", # Building part of bigger (tested) model. + "SegformerDecodeHead", # Building part of bigger (tested) model. + "PLBartEncoder", # Building part of bigger (tested) model. + "PLBartDecoder", # Building part of bigger (tested) model. + "PLBartDecoderWrapper", # Building part of bigger (tested) model. + "BigBirdPegasusEncoder", # Building part of bigger (tested) model. + "BigBirdPegasusDecoder", # Building part of bigger (tested) model. + "BigBirdPegasusDecoderWrapper", # Building part of bigger (tested) model. + "DetrEncoder", # Building part of bigger (tested) model. + "DetrDecoder", # Building part of bigger (tested) model. + "DetrDecoderWrapper", # Building part of bigger (tested) model. + "M2M100Encoder", # Building part of bigger (tested) model. + "M2M100Decoder", # Building part of bigger (tested) model. + "Speech2TextEncoder", # Building part of bigger (tested) model. + "Speech2TextDecoder", # Building part of bigger (tested) model. + "LEDEncoder", # Building part of bigger (tested) model. + "LEDDecoder", # Building part of bigger (tested) model. + "BartDecoderWrapper", # Building part of bigger (tested) model. + "BartEncoder", # Building part of bigger (tested) model. + "BertLMHeadModel", # Needs to be setup as decoder. + "BlenderbotSmallEncoder", # Building part of bigger (tested) model. + "BlenderbotSmallDecoderWrapper", # Building part of bigger (tested) model. + "BlenderbotEncoder", # Building part of bigger (tested) model. + "BlenderbotDecoderWrapper", # Building part of bigger (tested) model. + "MBartEncoder", # Building part of bigger (tested) model. + "MBartDecoderWrapper", # Building part of bigger (tested) model. + "MegatronBertLMHeadModel", # Building part of bigger (tested) model. + "MegatronBertEncoder", # Building part of bigger (tested) model. + "MegatronBertDecoder", # Building part of bigger (tested) model. + "MegatronBertDecoderWrapper", # Building part of bigger (tested) model. + "PegasusEncoder", # Building part of bigger (tested) model. + "PegasusDecoderWrapper", # Building part of bigger (tested) model. + "DPREncoder", # Building part of bigger (tested) model. + "ProphetNetDecoderWrapper", # Building part of bigger (tested) model. + "RealmBertModel", # Building part of bigger (tested) model. + "RealmReader", # Not regular model. + "RealmScorer", # Not regular model. + "RealmForOpenQA", # Not regular model. + "ReformerForMaskedLM", # Needs to be setup as decoder. + "Speech2Text2DecoderWrapper", # Building part of bigger (tested) model. + "TFDPREncoder", # Building part of bigger (tested) model. + "TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFModelMixin ?) + "TFRobertaForMultipleChoice", # TODO: fix + "TrOCRDecoderWrapper", # Building part of bigger (tested) model. + "SeparableConv1D", # Building part of bigger (tested) model. + "FlaxBartForCausalLM", # Building part of bigger (tested) model. + "FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM. + "OPTDecoderWrapper", +] + +# Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't +# trigger the common tests. +TEST_FILES_WITH_NO_COMMON_TESTS = [ + "models/decision_transformer/test_modeling_decision_transformer.py", + "models/camembert/test_modeling_camembert.py", + "models/mt5/test_modeling_flax_mt5.py", + "models/mbart/test_modeling_mbart.py", + "models/mt5/test_modeling_mt5.py", + "models/pegasus/test_modeling_pegasus.py", + "models/camembert/test_modeling_tf_camembert.py", + "models/mt5/test_modeling_tf_mt5.py", + "models/xlm_roberta/test_modeling_tf_xlm_roberta.py", + "models/xlm_roberta/test_modeling_flax_xlm_roberta.py", + "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py", + "models/xlm_roberta/test_modeling_xlm_roberta.py", + "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py", + "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py", + "models/decision_transformer/test_modeling_decision_transformer.py", +] + +# Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and +# should **not** be the rule. +IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [ + # models to ignore for model xxx mapping + "DPTForDepthEstimation", + "DecisionTransformerGPT2Model", + "GLPNForDepthEstimation", + "ViltForQuestionAnswering", + "ViltForImagesAndTextClassification", + "ViltForImageAndTextRetrieval", + "ViltForMaskedLM", + "XGLMEncoder", + "XGLMDecoder", + "XGLMDecoderWrapper", + "PerceiverForMultimodalAutoencoding", + "PerceiverForOpticalFlow", + "SegformerDecodeHead", + "FlaxBeitForMaskedImageModeling", + "PLBartEncoder", + "PLBartDecoder", + "PLBartDecoderWrapper", + "BeitForMaskedImageModeling", + "CLIPTextModel", + "CLIPVisionModel", + "TFCLIPTextModel", + "TFCLIPVisionModel", + "FlaxCLIPTextModel", + "FlaxCLIPVisionModel", + "FlaxWav2Vec2ForCTC", + "DetrForSegmentation", + "DPRReader", + "FlaubertForQuestionAnswering", + "FlavaImageCodebook", + "FlavaTextModel", + "FlavaImageModel", + "FlavaMultimodalModel", + "GPT2DoubleHeadsModel", + "LukeForMaskedLM", + "LukeForEntityClassification", + "LukeForEntityPairClassification", + "LukeForEntitySpanClassification", + "OpenAIGPTDoubleHeadsModel", + "RagModel", + "RagSequenceForGeneration", + "RagTokenForGeneration", + "RealmEmbedder", + "RealmForOpenQA", + "RealmScorer", + "RealmReader", + "TFDPRReader", + "TFGPT2DoubleHeadsModel", + "TFOpenAIGPTDoubleHeadsModel", + "TFRagModel", + "TFRagSequenceForGeneration", + "TFRagTokenForGeneration", + "Wav2Vec2ForCTC", + "HubertForCTC", + "SEWForCTC", + "SEWDForCTC", + "XLMForQuestionAnswering", + "XLNetForQuestionAnswering", + "SeparableConv1D", + "VisualBertForRegionToPhraseAlignment", + "VisualBertForVisualReasoning", + "VisualBertForQuestionAnswering", + "VisualBertForMultipleChoice", + "TFWav2Vec2ForCTC", + "TFHubertForCTC", + "MaskFormerForInstanceSegmentation", +] + +# Update this list for models that have multiple model types for the same +# model doc +MODEL_TYPE_TO_DOC_MAPPING = OrderedDict( + [ + ("data2vec-text", "data2vec"), + ("data2vec-audio", "data2vec"), + ("data2vec-vision", "data2vec"), + ] +) + + +# This is to make sure the transformers module imported is the one in the repo. +spec = importlib.util.spec_from_file_location( + "diffusers", + os.path.join(PATH_TO_DIFFUSERS, "__init__.py"), + submodule_search_locations=[PATH_TO_DIFFUSERS], +) +diffusers = spec.loader.load_module() + + +def check_model_list(): + """Check the model list inside the transformers library.""" + # Get the models from the directory structure of `src/diffusers/models/` + models_dir = os.path.join(PATH_TO_DIFFUSERS, "models") + _models = [] + for model in os.listdir(models_dir): + model_dir = os.path.join(models_dir, model) + if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): + _models.append(model) + + # Get the models from the directory structure of `src/transformers/models/` + models = [model for model in dir(diffusers.models) if not model.startswith("__")] + + missing_models = sorted(set(_models).difference(models)) + if missing_models: + raise Exception( + f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." + ) + + +# If some modeling modules should be ignored for all checks, they should be added in the nested list +# _ignore_modules of this function. +def get_model_modules(): + """Get the model modules inside the transformers library.""" + _ignore_modules = [ + "modeling_auto", + "modeling_encoder_decoder", + "modeling_marian", + "modeling_mmbt", + "modeling_outputs", + "modeling_retribert", + "modeling_utils", + "modeling_flax_auto", + "modeling_flax_encoder_decoder", + "modeling_flax_utils", + "modeling_speech_encoder_decoder", + "modeling_flax_speech_encoder_decoder", + "modeling_flax_vision_encoder_decoder", + "modeling_transfo_xl_utilities", + "modeling_tf_auto", + "modeling_tf_encoder_decoder", + "modeling_tf_outputs", + "modeling_tf_pytorch_utils", + "modeling_tf_utils", + "modeling_tf_transfo_xl_utilities", + "modeling_tf_vision_encoder_decoder", + "modeling_vision_encoder_decoder", + ] + modules = [] + for model in dir(diffusers.models): + # There are some magic dunder attributes in the dir, we ignore them + if not model.startswith("__"): + model_module = getattr(diffusers.models, model) + for submodule in dir(model_module): + if submodule.startswith("modeling") and submodule not in _ignore_modules: + modeling_module = getattr(model_module, submodule) + if inspect.ismodule(modeling_module): + modules.append(modeling_module) + return modules + + +def get_models(module, include_pretrained=False): + """Get the objects in module that are models.""" + models = [] + model_classes = (diffusers.ModelMixin, diffusers.TFModelMixin, diffusers.FlaxModelMixin) + for attr_name in dir(module): + if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name): + continue + attr = getattr(module, attr_name) + if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__: + models.append((attr_name, attr)) + return models + + +def is_a_private_model(model): + """Returns True if the model should not be in the main init.""" + if model in PRIVATE_MODELS: + return True + + # Wrapper, Encoder and Decoder are all privates + if model.endswith("Wrapper"): + return True + if model.endswith("Encoder"): + return True + if model.endswith("Decoder"): + return True + return False + + +def check_models_are_in_init(): + """Checks all models defined in the library are in the main init.""" + models_not_in_init = [] + dir_transformers = dir(diffusers) + for module in get_model_modules(): + models_not_in_init += [ + model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers + ] + + # Remove private models + models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)] + if len(models_not_in_init) > 0: + raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.") + + +# If some test_modeling files should be ignored when checking models are all tested, they should be added in the +# nested list _ignore_files of this function. +def get_model_test_files(): + """Get the model test files. + + The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be + considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files. + """ + + _ignore_files = [ + "test_modeling_common", + "test_modeling_encoder_decoder", + "test_modeling_flax_encoder_decoder", + "test_modeling_flax_speech_encoder_decoder", + "test_modeling_marian", + "test_modeling_tf_common", + "test_modeling_tf_encoder_decoder", + ] + test_files = [] + # Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models` + model_test_root = os.path.join(PATH_TO_TESTS, "models") + model_test_dirs = [] + for x in os.listdir(model_test_root): + x = os.path.join(model_test_root, x) + if os.path.isdir(x): + model_test_dirs.append(x) + + for target_dir in [PATH_TO_TESTS] + model_test_dirs: + for file_or_dir in os.listdir(target_dir): + path = os.path.join(target_dir, file_or_dir) + if os.path.isfile(path): + filename = os.path.split(path)[-1] + if "test_modeling" in filename and os.path.splitext(filename)[0] not in _ignore_files: + file = os.path.join(*path.split(os.sep)[1:]) + test_files.append(file) + + return test_files + + +# This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class +# for the all_model_classes variable. +def find_tested_models(test_file): + """Parse the content of test_file to detect what's in all_model_classes""" + # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class + with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f: + content = f.read() + all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content) + # Check with one less parenthesis as well + all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content) + if len(all_models) > 0: + model_tested = [] + for entry in all_models: + for line in entry.split(","): + name = line.strip() + if len(name) > 0: + model_tested.append(name) + return model_tested + + +def check_models_are_tested(module, test_file): + """Check models defined in module are tested in test_file.""" + # XxxModelMixin are not tested + defined_models = get_models(module) + tested_models = find_tested_models(test_file) + if tested_models is None: + if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS: + return + return [ + f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. " + + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file " + + "`utils/check_repo.py`." + ] + failures = [] + for model_name, _ in defined_models: + if model_name not in tested_models and model_name not in IGNORE_NON_TESTED: + failures.append( + f"{model_name} is defined in {module.__name__} but is not tested in " + + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file." + + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`" + + "in the file `utils/check_repo.py`." + ) + return failures + + +def check_all_models_are_tested(): + """Check all models are properly tested.""" + modules = get_model_modules() + test_files = get_model_test_files() + failures = [] + for module in modules: + test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file] + if len(test_file) == 0: + failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.") + elif len(test_file) > 1: + failures.append(f"{module.__name__} has several test files: {test_file}.") + else: + test_file = test_file[0] + new_failures = check_models_are_tested(module, test_file) + if new_failures is not None: + failures += new_failures + if len(failures) > 0: + raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) + + +def get_all_auto_configured_models(): + """Return the list of all models in at least one auto class.""" + result = set() # To avoid duplicates we concatenate all model classes in a set. + if is_torch_available(): + for attr_name in dir(diffusers.models.auto.modeling_auto): + if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"): + result = result | set(get_values(getattr(diffusers.models.auto.modeling_auto, attr_name))) + if is_flax_available(): + for attr_name in dir(diffusers.models.auto.modeling_flax_auto): + if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): + result = result | set(get_values(getattr(diffusers.models.auto.modeling_flax_auto, attr_name))) + return list(result) + + +def ignore_unautoclassed(model_name): + """Rules to determine if `name` should be in an auto class.""" + # Special white list + if model_name in IGNORE_NON_AUTO_CONFIGURED: + return True + # Encoder and Decoder should be ignored + if "Encoder" in model_name or "Decoder" in model_name: + return True + return False + + +def check_models_are_auto_configured(module, all_auto_models): + """Check models defined in module are each in an auto class.""" + defined_models = get_models(module) + failures = [] + for model_name, _ in defined_models: + if model_name not in all_auto_models and not ignore_unautoclassed(model_name): + failures.append( + f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. " + "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file " + "`utils/check_repo.py`." + ) + return failures + + +def check_all_models_are_auto_configured(): + """Check all models are each in an auto class.""" + missing_backends = [] + if not is_torch_available(): + missing_backends.append("PyTorch") + if not is_flax_available(): + missing_backends.append("Flax") + if len(missing_backends) > 0: + missing = ", ".join(missing_backends) + if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: + raise Exception( + "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " + f"Transformers repo, the following are missing: {missing}." + ) + else: + warnings.warn( + "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " + f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you " + "didn't make any change in one of those backends modeling files, you should probably execute the " + "command above to be on the safe side." + ) + modules = get_model_modules() + all_auto_models = get_all_auto_configured_models() + failures = [] + for module in modules: + new_failures = check_models_are_auto_configured(module, all_auto_models) + if new_failures is not None: + failures += new_failures + if len(failures) > 0: + raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) + + +_re_decorator = re.compile(r"^\s*@(\S+)\s+$") + + +def check_decorator_order(filename): + """Check that in the test file `filename` the slow decorator is always last.""" + with open(filename, "r", encoding="utf-8", newline="\n") as f: + lines = f.readlines() + decorator_before = None + errors = [] + for i, line in enumerate(lines): + search = _re_decorator.search(line) + if search is not None: + decorator_name = search.groups()[0] + if decorator_before is not None and decorator_name.startswith("parameterized"): + errors.append(i) + decorator_before = decorator_name + elif decorator_before is not None: + decorator_before = None + return errors + + +def check_all_decorator_order(): + """Check that in all test files, the slow decorator is always last.""" + errors = [] + for fname in os.listdir(PATH_TO_TESTS): + if fname.endswith(".py"): + filename = os.path.join(PATH_TO_TESTS, fname) + new_errors = check_decorator_order(filename) + errors += [f"- {filename}, line {i}" for i in new_errors] + if len(errors) > 0: + msg = "\n".join(errors) + raise ValueError( + "The parameterized decorator (and its variants) should always be first, but this is not the case in the" + f" following files:\n{msg}" + ) + + +def find_all_documented_objects(): + """Parse the content of all doc files to detect which classes and functions it documents""" + documented_obj = [] + for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"): + with open(doc_file, "r", encoding="utf-8", newline="\n") as f: + content = f.read() + raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content) + documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] + for doc_file in Path(PATH_TO_DOC).glob("**/*.md"): + with open(doc_file, "r", encoding="utf-8", newline="\n") as f: + content = f.read() + raw_doc_objs = re.findall(r"\[\[autodoc\]\]\s+(\S+)\s+", content) + documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] + return documented_obj + + +# One good reason for not being documented is to be deprecated. Put in this list deprecated objects. +DEPRECATED_OBJECTS = [ + "AutoModelWithLMHead", + "BartPretrainedModel", + "DataCollator", + "DataCollatorForSOP", + "GlueDataset", + "GlueDataTrainingArguments", + "LineByLineTextDataset", + "LineByLineWithRefDataset", + "LineByLineWithSOPTextDataset", + "PretrainedBartModel", + "PretrainedFSMTModel", + "SingleSentenceClassificationProcessor", + "SquadDataTrainingArguments", + "SquadDataset", + "SquadExample", + "SquadFeatures", + "SquadV1Processor", + "SquadV2Processor", + "TFAutoModelWithLMHead", + "TFBartPretrainedModel", + "TextDataset", + "TextDatasetForNextSentencePrediction", + "Wav2Vec2ForMaskedLM", + "Wav2Vec2Tokenizer", + "glue_compute_metrics", + "glue_convert_examples_to_features", + "glue_output_modes", + "glue_processors", + "glue_tasks_num_labels", + "squad_convert_examples_to_features", + "xnli_compute_metrics", + "xnli_output_modes", + "xnli_processors", + "xnli_tasks_num_labels", + "TFTrainer", + "TFTrainingArguments", +] + +# Exceptionally, some objects should not be documented after all rules passed. +# ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT! +UNDOCUMENTED_OBJECTS = [ + "AddedToken", # This is a tokenizers class. + "BasicTokenizer", # Internal, should never have been in the main init. + "CharacterTokenizer", # Internal, should never have been in the main init. + "DPRPretrainedReader", # Like an Encoder. + "DummyObject", # Just picked by mistake sometimes. + "MecabTokenizer", # Internal, should never have been in the main init. + "ModelCard", # Internal type. + "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer) + "TFDPRPretrainedReader", # Like an Encoder. + "TransfoXLCorpus", # Internal type. + "WordpieceTokenizer", # Internal, should never have been in the main init. + "absl", # External module + "add_end_docstrings", # Internal, should never have been in the main init. + "add_start_docstrings", # Internal, should never have been in the main init. + "cached_path", # Internal used for downloading models. + "convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights + "logger", # Internal logger + "logging", # External module + "requires_backends", # Internal function +] + +# This list should be empty. Objects in it should get their own doc page. +SHOULD_HAVE_THEIR_OWN_PAGE = [ + # Benchmarks + "PyTorchBenchmark", + "PyTorchBenchmarkArguments", + "TensorFlowBenchmark", + "TensorFlowBenchmarkArguments", +] + + +def ignore_undocumented(name): + """Rules to determine if `name` should be undocumented.""" + # NOT DOCUMENTED ON PURPOSE. + # Constants uppercase are not documented. + if name.isupper(): + return True + # ModelMixins / Encoders / Decoders / Layers / Embeddings / Attention are not documented. + if ( + name.endswith("ModelMixin") + or name.endswith("Decoder") + or name.endswith("Encoder") + or name.endswith("Layer") + or name.endswith("Embeddings") + or name.endswith("Attention") + ): + return True + # Submodules are not documented. + if os.path.isdir(os.path.join(PATH_TO_DIFFUSERS, name)) or os.path.isfile( + os.path.join(PATH_TO_DIFFUSERS, f"{name}.py") + ): + return True + # All load functions are not documented. + if name.startswith("load_tf") or name.startswith("load_pytorch"): + return True + # is_xxx_available functions are not documented. + if name.startswith("is_") and name.endswith("_available"): + return True + # Deprecated objects are not documented. + if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: + return True + # MMBT model does not really work. + if name.startswith("MMBT"): + return True + if name in SHOULD_HAVE_THEIR_OWN_PAGE: + return True + return False + + +def check_all_objects_are_documented(): + """Check all models are properly documented.""" + documented_objs = find_all_documented_objects() + modules = diffusers._modules + objects = [c for c in dir(diffusers) if c not in modules and not c.startswith("_")] + undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)] + if len(undocumented_objs) > 0: + raise Exception( + "The following objects are in the public init so should be documented:\n - " + + "\n - ".join(undocumented_objs) + ) + check_docstrings_are_in_md() + check_model_type_doc_match() + + +def check_model_type_doc_match(): + """Check all doc pages have a corresponding model type.""" + model_doc_folder = Path(PATH_TO_DOC) / "model_doc" + model_docs = [m.stem for m in model_doc_folder.glob("*.md")] + + model_types = list(diffusers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys()) + model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types] + + errors = [] + for m in model_docs: + if m not in model_types and m != "auto": + close_matches = get_close_matches(m, model_types) + error_message = f"{m} is not a proper model identifier." + if len(close_matches) > 0: + close_matches = "/".join(close_matches) + error_message += f" Did you mean {close_matches}?" + errors.append(error_message) + + if len(errors) > 0: + raise ValueError( + "Some model doc pages do not match any existing model type:\n" + + "\n".join(errors) + + "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in " + "models/auto/configuration_auto.py." + ) + + +# Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`. +_re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`") +# Re pattern to catch things between double backquotes. +_re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)") +# Re pattern to catch example introduction. +_re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE) + + +def is_rst_docstring(docstring): + """ + Returns `True` if `docstring` is written in rst. + """ + if _re_rst_special_words.search(docstring) is not None: + return True + if _re_double_backquotes.search(docstring) is not None: + return True + if _re_rst_example.search(docstring) is not None: + return True + return False + + +def check_docstrings_are_in_md(): + """Check all docstrings are in md""" + files_with_rst = [] + for file in Path(PATH_TO_DIFFUSERS).glob("**/*.py"): + with open(file, "r") as f: + code = f.read() + docstrings = code.split('"""') + + for idx, docstring in enumerate(docstrings): + if idx % 2 == 0 or not is_rst_docstring(docstring): + continue + files_with_rst.append(file) + break + + if len(files_with_rst) > 0: + raise ValueError( + "The following files have docstrings written in rst:\n" + + "\n".join([f"- {f}" for f in files_with_rst]) + + "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n" + "(`pip install git+https://github.com/huggingface/doc-builder`)" + ) + + +def check_repo_quality(): + """Check all models are properly tested and documented.""" + print("Checking all models are included.") + check_model_list() + print("Checking all models are public.") + check_models_are_in_init() + print("Checking all models are properly tested.") + check_all_decorator_order() + check_all_models_are_tested() + print("Checking all objects are properly documented.") + check_all_objects_are_documented() + print("Checking all models are in at least one auto class.") + check_all_models_are_auto_configured() + + +if __name__ == "__main__": + check_repo_quality() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_support_list.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_support_list.py new file mode 100644 index 0000000000000000000000000000000000000000..ade9df3b64faa2e9daa227e36c036df4e69116bc --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_support_list.py @@ -0,0 +1,124 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +""" +Utility that checks that modules like attention processors are listed in the documentation file. + +```bash +python utils/check_support_list.py +``` + +It has no auto-fix mode. +""" + +import os +import re + + +# All paths are set with the intent that you run this script from the root of the repo +REPO_PATH = "." + + +def read_documented_classes(doc_path, autodoc_regex=r"\[\[autodoc\]\]\s([^\n]+)"): + """ + Reads documented classes from a doc file using a regex to find lines like [[autodoc]] my.module.Class. + Returns a list of documented class names (just the class name portion). + """ + with open(os.path.join(REPO_PATH, doc_path), "r") as f: + doctext = f.read() + matches = re.findall(autodoc_regex, doctext) + return [match.split(".")[-1] for match in matches] + + +def read_source_classes(src_path, class_regex, exclude_conditions=None): + """ + Reads class names from a source file using a regex that captures class definitions. + Optionally exclude classes based on a list of conditions (functions that take class name and return bool). + """ + if exclude_conditions is None: + exclude_conditions = [] + with open(os.path.join(REPO_PATH, src_path), "r") as f: + doctext = f.read() + classes = re.findall(class_regex, doctext) + # Filter out classes that meet any of the exclude conditions + filtered_classes = [c for c in classes if not any(cond(c) for cond in exclude_conditions)] + return filtered_classes + + +def check_documentation(doc_path, src_path, doc_regex, src_regex, exclude_conditions=None): + """ + Generic function to check if all classes defined in `src_path` are documented in `doc_path`. + Returns a set of undocumented class names. + """ + documented = set(read_documented_classes(doc_path, doc_regex)) + source_classes = set(read_source_classes(src_path, src_regex, exclude_conditions=exclude_conditions)) + + # Find which classes in source are not documented in a deterministic way. + undocumented = sorted(source_classes - documented) + return undocumented + + +if __name__ == "__main__": + # Define the checks we need to perform + checks = { + "Attention Processors": { + "doc_path": "docs/source/en/api/attnprocessor.md", + "src_path": "src/diffusers/models/attention_processor.py", + "doc_regex": r"\[\[autodoc\]\]\s([^\n]+)", + "src_regex": r"class\s+(\w+Processor(?:\d*_?\d*))[:(]", + "exclude_conditions": [lambda c: "LoRA" in c, lambda c: c == "Attention"], + }, + "Image Processors": { + "doc_path": "docs/source/en/api/image_processor.md", + "src_path": "src/diffusers/image_processor.py", + "doc_regex": r"\[\[autodoc\]\]\s([^\n]+)", + "src_regex": r"class\s+(\w+Processor(?:\d*_?\d*))[:(]", + }, + "Activations": { + "doc_path": "docs/source/en/api/activations.md", + "src_path": "src/diffusers/models/activations.py", + "doc_regex": r"\[\[autodoc\]\]\s([^\n]+)", + "src_regex": r"class\s+(\w+)\s*\(.*?nn\.Module.*?\):", + }, + "Normalizations": { + "doc_path": "docs/source/en/api/normalization.md", + "src_path": "src/diffusers/models/normalization.py", + "doc_regex": r"\[\[autodoc\]\]\s([^\n]+)", + "src_regex": r"class\s+(\w+)\s*\(.*?nn\.Module.*?\):", + "exclude_conditions": [ + # Exclude LayerNorm as it's an intentional exception + lambda c: c == "LayerNorm" + ], + }, + "LoRA Mixins": { + "doc_path": "docs/source/en/api/loaders/lora.md", + "src_path": "src/diffusers/loaders/lora_pipeline.py", + "doc_regex": r"\[\[autodoc\]\]\s([^\n]+)", + "src_regex": r"class\s+(\w+LoraLoaderMixin(?:\d*_?\d*))[:(]", + }, + } + + missing_items = {} + for category, params in checks.items(): + undocumented = check_documentation( + doc_path=params["doc_path"], + src_path=params["src_path"], + doc_regex=params["doc_regex"], + src_regex=params["src_regex"], + exclude_conditions=params.get("exclude_conditions"), + ) + if undocumented: + missing_items[category] = undocumented + + # If we have any missing items, raise a single combined error + if missing_items: + error_msg = ["Some classes are not documented properly:\n"] + for category, classes in missing_items.items(): + error_msg.append(f"- {category}: {', '.join(sorted(classes))}") + raise ValueError("\n".join(error_msg)) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_table.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_table.py new file mode 100644 index 0000000000000000000000000000000000000000..83c29aa74eca042df81386a73c12ffaf128fc4c2 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/check_table.py @@ -0,0 +1,185 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import collections +import importlib.util +import os +import re + + +# All paths are set with the intent you should run this script from the root of the repo with the command +# python utils/check_table.py +TRANSFORMERS_PATH = "src/diffusers" +PATH_TO_DOCS = "docs/source/en" +REPO_PATH = "." + + +def _find_text_in_file(filename, start_prompt, end_prompt): + """ + Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty + lines. + """ + with open(filename, "r", encoding="utf-8", newline="\n") as f: + lines = f.readlines() + # Find the start prompt. + start_index = 0 + while not lines[start_index].startswith(start_prompt): + start_index += 1 + start_index += 1 + + end_index = start_index + while not lines[end_index].startswith(end_prompt): + end_index += 1 + end_index -= 1 + + while len(lines[start_index]) <= 1: + start_index += 1 + while len(lines[end_index]) <= 1: + end_index -= 1 + end_index += 1 + return "".join(lines[start_index:end_index]), start_index, end_index, lines + + +# Add here suffixes that are used to identify models, separated by | +ALLOWED_MODEL_SUFFIXES = "Model|Encoder|Decoder|ForConditionalGeneration" +# Regexes that match TF/Flax/PT model names. +_re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") +_re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") +# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes. +_re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") + + +# This is to make sure the diffusers module imported is the one in the repo. +spec = importlib.util.spec_from_file_location( + "diffusers", + os.path.join(TRANSFORMERS_PATH, "__init__.py"), + submodule_search_locations=[TRANSFORMERS_PATH], +) +diffusers_module = spec.loader.load_module() + + +# Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python +def camel_case_split(identifier): + """Split a camelcased `identifier` into words.""" + matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier) + return [m.group(0) for m in matches] + + +def _center_text(text, width): + text_length = 2 if text == "✅" or text == "❌" else len(text) + left_indent = (width - text_length) // 2 + right_indent = width - text_length - left_indent + return " " * left_indent + text + " " * right_indent + + +def get_model_table_from_auto_modules(): + """Generates an up-to-date model table from the content of the auto modules.""" + # Dictionary model names to config. + config_mapping_names = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES + model_name_to_config = { + name: config_mapping_names[code] + for code, name in diffusers_module.MODEL_NAMES_MAPPING.items() + if code in config_mapping_names + } + model_name_to_prefix = {name: config.replace("ConfigMixin", "") for name, config in model_name_to_config.items()} + + # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax. + slow_tokenizers = collections.defaultdict(bool) + fast_tokenizers = collections.defaultdict(bool) + pt_models = collections.defaultdict(bool) + tf_models = collections.defaultdict(bool) + flax_models = collections.defaultdict(bool) + + # Let's lookup through all diffusers object (once). + for attr_name in dir(diffusers_module): + lookup_dict = None + if attr_name.endswith("Tokenizer"): + lookup_dict = slow_tokenizers + attr_name = attr_name[:-9] + elif attr_name.endswith("TokenizerFast"): + lookup_dict = fast_tokenizers + attr_name = attr_name[:-13] + elif _re_tf_models.match(attr_name) is not None: + lookup_dict = tf_models + attr_name = _re_tf_models.match(attr_name).groups()[0] + elif _re_flax_models.match(attr_name) is not None: + lookup_dict = flax_models + attr_name = _re_flax_models.match(attr_name).groups()[0] + elif _re_pt_models.match(attr_name) is not None: + lookup_dict = pt_models + attr_name = _re_pt_models.match(attr_name).groups()[0] + + if lookup_dict is not None: + while len(attr_name) > 0: + if attr_name in model_name_to_prefix.values(): + lookup_dict[attr_name] = True + break + # Try again after removing the last word in the name + attr_name = "".join(camel_case_split(attr_name)[:-1]) + + # Let's build that table! + model_names = list(model_name_to_config.keys()) + model_names.sort(key=str.lower) + columns = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"] + # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side). + widths = [len(c) + 2 for c in columns] + widths[0] = max([len(name) for name in model_names]) + 2 + + # Build the table per se + table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n" + # Use ":-----:" format to center-aligned table cell texts + table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n" + + check = {True: "✅", False: "❌"} + for name in model_names: + prefix = model_name_to_prefix[name] + line = [ + name, + check[slow_tokenizers[prefix]], + check[fast_tokenizers[prefix]], + check[pt_models[prefix]], + check[tf_models[prefix]], + check[flax_models[prefix]], + ] + table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n" + return table + + +def check_model_table(overwrite=False): + """Check the model table in the index.rst is consistent with the state of the lib and maybe `overwrite`.""" + current_table, start_index, end_index, lines = _find_text_in_file( + filename=os.path.join(PATH_TO_DOCS, "index.md"), + start_prompt="", + ) + new_table = get_model_table_from_auto_modules() + + if current_table != new_table: + if overwrite: + with open(os.path.join(PATH_TO_DOCS, "index.md"), "w", encoding="utf-8", newline="\n") as f: + f.writelines(lines[:start_index] + [new_table] + lines[end_index:]) + else: + raise ValueError( + "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this." + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") + args = parser.parse_args() + + check_model_table(args.fix_and_overwrite) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/consolidated_test_report.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/consolidated_test_report.py new file mode 100644 index 0000000000000000000000000000000000000000..134fecf721e44ab50c16d8ed3469c94c74ffd408 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/consolidated_test_report.py @@ -0,0 +1,789 @@ +#!/usr/bin/env python +import argparse +import glob +import os +import re +from datetime import date, datetime + +from slack_sdk import WebClient +from tabulate import tabulate + + +MAX_LEN_MESSAGE = 3001 # slack endpoint has a limit of 3001 characters + +parser = argparse.ArgumentParser() +parser.add_argument("--slack_channel_name", default="diffusers-ci-nightly") +parser.add_argument( + "--reports_dir", + default="reports", + help="Directory containing test reports (will search recursively in all subdirectories)", +) +parser.add_argument("--output_file", default=None, help="Path to save the consolidated report (markdown format)") + + +def parse_stats_file(file_path): + """Parse a stats file to extract test statistics.""" + try: + with open(file_path, "r") as f: + content = f.read() + + # Extract the numbers using regex + tests_pattern = r"collected (\d+) items" + passed_pattern = r"(\d+) passed" + failed_pattern = r"(\d+) failed" + skipped_pattern = r"(\d+) skipped" + xpassed_pattern = r"(\d+) xpassed" + + tests_match = re.search(tests_pattern, content) + passed_match = re.search(passed_pattern, content) + failed_match = re.search(failed_pattern, content) + skipped_match = re.search(skipped_pattern, content) + xpassed_match = re.search(xpassed_pattern, content) + + passed = int(passed_match.group(1)) if passed_match else 0 + failed = int(failed_match.group(1)) if failed_match else 0 + skipped = int(skipped_match.group(1)) if skipped_match else 0 + xpassed = int(xpassed_match.group(1)) if xpassed_match else 0 + + # If tests_match exists, use it, otherwise calculate from passed/failed/skipped + if tests_match: + tests = int(tests_match.group(1)) + else: + tests = passed + failed + skipped + xpassed + + # Extract timing information if available + timing_pattern = r"slowest \d+ test durations[\s\S]*?\n([\s\S]*?)={70}" + timing_match = re.search(timing_pattern, content, re.MULTILINE) + slowest_tests = [] + + if timing_match: + timing_text = timing_match.group(1).strip() + test_timing_lines = timing_text.split("\n") + for line in test_timing_lines: + if line.strip(): + # Format is typically: 10.37s call tests/path/to/test.py::TestClass::test_method + parts = line.strip().split() + if len(parts) >= 3: + time_str = parts[0] + test_path = " ".join(parts[2:]) + + # Skip entries with "< 0.05 secs were omitted" or similar + if "secs were omitted" in test_path: + continue + + try: + time_seconds = float(time_str.rstrip("s")) + slowest_tests.append({"test": test_path, "duration": time_seconds}) + except ValueError: + pass + + return { + "tests": tests, + "passed": passed, + "failed": failed, + "skipped": skipped, + "slowest_tests": slowest_tests, + } + except Exception as e: + print(f"Error parsing {file_path}: {e}") + return {"tests": 0, "passed": 0, "failed": 0, "skipped": 0, "slowest_tests": []} + + +def parse_durations_file(file_path): + """Parse a durations file to extract test timing information.""" + slowest_tests = [] + try: + durations_file = file_path.replace("_stats.txt", "_durations.txt") + if os.path.exists(durations_file): + with open(durations_file, "r") as f: + content = f.read() + + # Skip the header line + for line in content.split("\n")[1:]: + if line.strip(): + # Format is typically: 10.37s call tests/path/to/test.py::TestClass::test_method + parts = line.strip().split() + if len(parts) >= 3: + time_str = parts[0] + test_path = " ".join(parts[2:]) + + # Skip entries with "< 0.05 secs were omitted" or similar + if "secs were omitted" in test_path: + continue + + try: + time_seconds = float(time_str.rstrip("s")) + slowest_tests.append({"test": test_path, "duration": time_seconds}) + except ValueError: + # If time_str is not a valid float, it might be a different format + # For example, some pytest formats show "< 0.05s" or similar + if test_path.startswith("<") and "secs were omitted" in test_path: + # Extract the time value from test_path if it's in the format "< 0.05 secs were omitted" + try: + # This handles entries where the time is in the test_path itself + dur_match = re.search(r"(\d+(?:\.\d+)?)", test_path) + if dur_match: + time_seconds = float(dur_match.group(1)) + slowest_tests.append({"test": test_path, "duration": time_seconds}) + except ValueError: + pass + except Exception as e: + print(f"Error parsing durations file {file_path.replace('_stats.txt', '_durations.txt')}: {e}") + + return slowest_tests + + +def parse_failures_file(file_path): + """Parse a failures file to extract failed test details.""" + failures = [] + try: + with open(file_path, "r") as f: + content = f.read() + + # We don't need the base file name anymore as we're getting test paths from summary + + # Check if it's a short stack format + if "============================= FAILURES SHORT STACK =============================" in content: + # First, look for pytest-style failure headers with underscores and clean them up + test_headers = re.findall(r"_{5,}\s+([^_\n]+?)\s+_{5,}", content) + + for test_name in test_headers: + test_name = test_name.strip() + # Make sure it's a valid test name (contains a dot and doesn't look like a number) + if "." in test_name and not test_name.replace(".", "").isdigit(): + # For test names missing the full path, check if we can reconstruct it from failures_line.txt + # This is a best effort - we won't always have the line file available + if not test_name.endswith(".py") and "::" not in test_name and "/" not in test_name: + # Try to look for a corresponding line file + line_file = file_path.replace("_failures_short.txt", "_failures_line.txt") + if os.path.exists(line_file): + try: + with open(line_file, "r") as lf: + line_content = lf.read() + # Look for test name in line file which might have the full path + path_match = re.search( + r"(tests/[\w/]+\.py::[^:]+::" + test_name.split(".")[-1] + ")", + line_content, + ) + if path_match: + test_name = path_match.group(1) + except Exception: + pass # If we can't read the line file, just use what we have + + failures.append( + { + "test": test_name, + "error": "Error occurred", + "original_test_name": test_name, # Keep original for reference + } + ) + + # If we didn't find any pytest-style headers, try other formats + if not failures: + # Look for test names at the beginning of the file (in first few lines) + first_lines = content.split("\n")[:20] # Look at first 20 lines + for line in first_lines: + # Look for test names in various formats + # Format: tests/file.py::TestClass::test_method + path_match = re.search(r"(tests/[\w/]+\.py::[\w\.]+::\w+)", line) + # Format: TestClass.test_method + class_match = re.search(r"([A-Za-z][A-Za-z0-9_]+\.[A-Za-z][A-Za-z0-9_]+)", line) + + if path_match: + test_name = path_match.group(1) + failures.append( + {"test": test_name, "error": "Error occurred", "original_test_name": test_name} + ) + break # Found a full path, stop looking + elif class_match and "test" in line.lower(): + test_name = class_match.group(1) + # Make sure it's likely a test name (contains test in method name) + if "test" in test_name.lower(): + failures.append( + {"test": test_name, "error": "Error occurred", "original_test_name": test_name} + ) + else: + # Standard format - try to extract from standard pytest output + failure_blocks = re.split(r"={70}", content) + + for block in failure_blocks: + if not block.strip(): + continue + + # Look for test paths in the format: path/to/test.py::TestClass::test_method + path_matches = re.findall(r"([\w/]+\.py::[\w\.]+::\w+)", block) + if path_matches: + for test_name in path_matches: + failures.append( + {"test": test_name, "error": "Error occurred", "original_test_name": test_name} + ) + else: + # Try alternative format: TestClass.test_method + class_matches = re.findall(r"([A-Za-z][A-Za-z0-9_]+\.[A-Za-z][A-Za-z0-9_]+)", block) + for test_name in class_matches: + # Filter out things that don't look like test names + if ( + not test_name.startswith(("e.g", "i.e", "etc.")) + and not test_name.isdigit() + and "test" in test_name.lower() + ): + failures.append( + {"test": test_name, "error": "Error occurred", "original_test_name": test_name} + ) + + except Exception as e: + print(f"Error parsing failures in {file_path}: {e}") + + return failures + + +def consolidate_reports(reports_dir): + """Consolidate test reports from multiple test runs, including from subdirectories.""" + # Get all stats files, including those in subdirectories + stats_files = glob.glob(f"{reports_dir}/**/*_stats.txt", recursive=True) + + results = {} + total_stats = {"tests": 0, "passed": 0, "failed": 0, "skipped": 0} + + # Collect all slow tests across all test suites + all_slow_tests = [] + + # Process each stats file and its corresponding failures file + for stats_file in stats_files: + # Extract test suite name from filename (e.g., tests_pipeline_allegro_cuda_stats.txt -> pipeline_allegro_cuda) + base_name = os.path.basename(stats_file).replace("_stats.txt", "") + + # Include parent directory in suite name if it's in a subdirectory + rel_path = os.path.relpath(os.path.dirname(stats_file), reports_dir) + if rel_path and rel_path != ".": + # Remove 'test_reports' suffix from directory name if present + dir_name = os.path.basename(rel_path) + if dir_name.endswith("_test_reports"): + dir_name = dir_name[:-13] # Remove '_test_reports' suffix + base_name = f"{dir_name}/{base_name}" + + # Parse stats + stats = parse_stats_file(stats_file) + + # If no slowest tests found in stats file, try the durations file directly + if not stats.get("slowest_tests"): + stats["slowest_tests"] = parse_durations_file(stats_file) + + # Update total stats + for key in ["tests", "passed", "failed", "skipped"]: + total_stats[key] += stats[key] + + # Collect slowest tests with their suite name + for slow_test in stats.get("slowest_tests", []): + all_slow_tests.append({"test": slow_test["test"], "duration": slow_test["duration"], "suite": base_name}) + + # Parse failures if there are any + failures = [] + if stats["failed"] > 0: + # First try to get test paths from summary_short.txt which has the best format + summary_file = stats_file.replace("_stats.txt", "_summary_short.txt") + if os.path.exists(summary_file): + try: + with open(summary_file, "r") as f: + content = f.read() + # Look for full lines with test path and error message: "FAILED test_path - error_msg" + failed_test_lines = re.findall( + r"FAILED\s+(tests/[\w/]+\.py::[A-Za-z0-9_\.]+::[A-Za-z0-9_]+)(?:\s+-\s+(.+))?", content + ) + + if failed_test_lines: + for match in failed_test_lines: + test_path = match[0] + error_msg = match[1] if len(match) > 1 and match[1] else "No error message" + + failures.append({"test": test_path, "error": error_msg}) + except Exception as e: + print(f"Error parsing summary file: {e}") + + # If no failures found in summary, try other failure files + if not failures: + failure_patterns = ["_failures_short.txt", "_failures.txt", "_failures_line.txt", "_failures_long.txt"] + + for pattern in failure_patterns: + failures_file = stats_file.replace("_stats.txt", pattern) + if os.path.exists(failures_file): + failures = parse_failures_file(failures_file) + if failures: + break + + # No debug output needed + + # Store results for this test suite + results[base_name] = {"stats": stats, "failures": failures} + + # Filter out entries with "secs were omitted" + filtered_slow_tests = [test for test in all_slow_tests if "secs were omitted" not in test["test"]] + + # Sort all slow tests by duration (descending) + filtered_slow_tests.sort(key=lambda x: x["duration"], reverse=True) + + # Get the number of slowest tests to show from environment variable or default to 10 + num_slowest_tests = int(os.environ.get("SHOW_SLOWEST_TESTS", "10")) + top_slowest_tests = filtered_slow_tests[:num_slowest_tests] if filtered_slow_tests else [] + + # Calculate additional duration statistics + total_duration = sum(test["duration"] for test in all_slow_tests) + + # Calculate duration per suite + suite_durations = {} + for test in all_slow_tests: + suite_name = test["suite"] + if suite_name not in suite_durations: + suite_durations[suite_name] = 0 + suite_durations[suite_name] += test["duration"] + + # Removed duration categories + + return { + "total_stats": total_stats, + "test_suites": results, + "slowest_tests": top_slowest_tests, + "duration_stats": {"total_duration": total_duration, "suite_durations": suite_durations}, + } + + +def generate_report(consolidated_data): + """Generate a comprehensive markdown report from consolidated data.""" + report = [] + + # Add report header + report.append("# Diffusers Nightly Test Report") + report.append(f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") + + # Removed comparison section + + # Add summary section + total = consolidated_data["total_stats"] + report.append("## Summary") + + # Get duration stats if available + duration_stats = consolidated_data.get("duration_stats", {}) + total_duration = duration_stats.get("total_duration", 0) + + summary_table = [ + ["Total Tests", total["tests"]], + ["Passed", total["passed"]], + ["Failed", total["failed"]], + ["Skipped", total["skipped"]], + ["Success Rate", f"{(total['passed'] / total['tests'] * 100):.2f}%" if total["tests"] > 0 else "N/A"], + ["Total Duration", f"{total_duration:.2f}s" if total_duration else "N/A"], + ] + + report.append(tabulate(summary_table, tablefmt="pipe")) + report.append("") + + # Removed duration distribution section + + # Add test suites summary + report.append("## Test Suites") + + # Include duration in test suites table if available + suite_durations = consolidated_data.get("duration_stats", {}).get("suite_durations", {}) + + if suite_durations: + suites_table = [["Test Suite", "Tests", "Passed", "Failed", "Skipped", "Success Rate", "Duration (s)"]] + else: + suites_table = [["Test Suite", "Tests", "Passed", "Failed", "Skipped", "Success Rate"]] + + # Sort test suites by success rate (ascending - least successful first) + sorted_suites = sorted( + consolidated_data["test_suites"].items(), + key=lambda x: (x[1]["stats"]["passed"] / x[1]["stats"]["tests"] * 100) if x[1]["stats"]["tests"] > 0 else 0, + reverse=False, + ) + + for suite_name, suite_data in sorted_suites: + stats = suite_data["stats"] + success_rate = f"{(stats['passed'] / stats['tests'] * 100):.2f}%" if stats["tests"] > 0 else "N/A" + + if suite_durations: + duration = suite_durations.get(suite_name, 0) + suites_table.append( + [ + suite_name, + stats["tests"], + stats["passed"], + stats["failed"], + stats["skipped"], + success_rate, + f"{duration:.2f}", + ] + ) + else: + suites_table.append( + [suite_name, stats["tests"], stats["passed"], stats["failed"], stats["skipped"], success_rate] + ) + + report.append(tabulate(suites_table, headers="firstrow", tablefmt="pipe")) + report.append("") + + # Add slowest tests section + slowest_tests = consolidated_data.get("slowest_tests", []) + if slowest_tests: + report.append("## Slowest Tests") + + slowest_table = [["Rank", "Test", "Duration (s)", "Test Suite"]] + for i, test in enumerate(slowest_tests, 1): + # Skip entries that don't contain actual test names + if "< 0.05 secs were omitted" in test["test"]: + continue + slowest_table.append([i, test["test"], f"{test['duration']:.2f}", test["suite"]]) + + report.append(tabulate(slowest_table, headers="firstrow", tablefmt="pipe")) + report.append("") + + # Add failures section if there are any + failed_suites = [s for s in sorted_suites if s[1]["stats"]["failed"] > 0] + + if failed_suites: + report.append("## Failures") + + # Group failures by module for cleaner organization + failures_by_module = {} + + for suite_name, suite_data in failed_suites: + # Extract failures data for this suite + for failure in suite_data.get("failures", []): + test_name = failure["test"] + + # If test name doesn't look like a full path, try to reconstruct it + if not ("/" in test_name or "::" in test_name) and "." in test_name: + # For simple 'TestClass.test_method' format, try to get full path from suite name + # Form: tests__cuda -> tests//test_.py::TestClass::test_method + if suite_name.startswith("tests_") and "_cuda" in suite_name: + # Extract component name from suite + component = suite_name.replace("tests_", "").replace("_cuda", "") + if "." in test_name: + class_name, method_name = test_name.split(".", 1) + possible_path = f"tests/{component}/test_{component}.py::{class_name}::{method_name}" + # Use this constructed path if it seems reasonable + if "test_" in method_name: + test_name = possible_path + + # Extract module name from test name + if "::" in test_name: + # For path/file.py::TestClass::test_method format + parts = test_name.split("::") + module_name = parts[-2] if len(parts) >= 2 else "Other" # TestClass + elif "." in test_name: + # For TestClass.test_method format + parts = test_name.split(".") + module_name = parts[0] # TestClass + else: + module_name = "Other" + + # Skip module names that don't look like class/module names + if ( + module_name.startswith(("e.g", "i.e", "etc")) + or module_name.replace(".", "").isdigit() + or len(module_name) < 3 + ): + module_name = "Other" + + # Add to the module group + if module_name not in failures_by_module: + failures_by_module[module_name] = [] + + # Prepend the suite name if the test name doesn't already have a full path + if "/" not in test_name and suite_name not in test_name: + full_test_name = f"{suite_name}::{test_name}" + else: + full_test_name = test_name + + # Add this failure to the module group + failures_by_module[module_name].append( + {"test": full_test_name, "original_test": test_name, "error": failure["error"]} + ) + + # Create a list of failing tests for each module + if failures_by_module: + for module_name, failures in sorted(failures_by_module.items()): + report.append(f"### {module_name}") + + # Put all failed tests in a single code block + report.append("```") + for failure in failures: + # Show test path and error message if available + if failure.get("error") and failure["error"] != "No error message": + report.append(f"{failure['test']} - {failure['error']}") + else: + report.append(failure["test"]) + report.append("```") + + report.append("") # Add space between modules + else: + report.append("*No detailed failure information available*") + report.append("") + + return "\n".join(report) + + +def create_test_groups_table(test_groups, total_tests, total_success_rate): + """Create a table-like format for test groups showing total tests and success rate.""" + if not test_groups: + return None + + # Sort by total test count (descending) + sorted_groups = sorted(test_groups.items(), key=lambda x: x[1]["total"], reverse=True) + + # Create table lines + table_lines = ["```"] + table_lines.append("Test Results Summary") + table_lines.append("-------------------") + table_lines.append(f"Total Tests: {total_tests:,}") + table_lines.append(f"Success Rate: {total_success_rate}") + table_lines.append("") + table_lines.append("Category | Total Tests | Failed | Success Rate") + table_lines.append("------------------- | ----------- | ------ | ------------") + + # Add rows + for category, stats in sorted_groups: + # Pad category name to fixed width (19 chars) + padded_cat = category[:19].ljust(19) # Truncate if too long + # Right-align counts + padded_total = str(stats["total"]).rjust(11) + padded_failed = str(stats["failed"]).rjust(6) + # Calculate and format success rate + if stats["total"] > 0: + cat_success_rate = f"{((stats['total'] - stats['failed']) / stats['total'] * 100):.1f}%" + else: + cat_success_rate = "N/A" + padded_rate = cat_success_rate.rjust(12) + table_lines.append(f"{padded_cat} | {padded_total} | {padded_failed} | {padded_rate}") + + table_lines.append("```") + + total_failures = sum(stats["failed"] for stats in test_groups.values()) + return ( + f"*Test Groups Summary ({total_failures} {'failure' if total_failures == 1 else 'failures'}):*\n" + + "\n".join(table_lines) + ) + + +def create_slack_payload(consolidated_data): + """Create a concise Slack message payload from consolidated data.""" + total = consolidated_data["total_stats"] + success_rate = f"{(total['passed'] / total['tests'] * 100):.2f}%" if total["tests"] > 0 else "N/A" + + # Determine emoji based on success rate + if total["failed"] == 0: + emoji = "✅" + elif total["failed"] / total["tests"] < 0.1: + emoji = "⚠️" + else: + emoji = "❌" + + # Create a more compact summary section + summary = f"{emoji} *Diffusers Nightly Tests:* {success_rate} success ({total['passed']}/{total['tests']} tests" + if total["skipped"] > 0: + summary += f", {total['skipped']} skipped" + summary += ")" + + # Create the test suites table in markdown format + # Build the markdown table with proper alignment + table_lines = [] + table_lines.append("```") + + # Sort test suites by success rate (ascending - least successful first) + sorted_suites = sorted( + consolidated_data["test_suites"].items(), + key=lambda x: (x[1]["stats"]["passed"] / x[1]["stats"]["tests"] * 100) if x[1]["stats"]["tests"] > 0 else 0, + reverse=False, + ) + + # Calculate max widths for proper alignment + max_suite_name_len = max(len(suite_name) for suite_name, _ in sorted_suites) if sorted_suites else 10 + max_suite_name_len = max(max_suite_name_len, len("Test Suite")) # Ensure header fits + + # Create header with proper spacing (only Tests, Failed, Success Rate) + header = f"| {'Test Suite'.ljust(max_suite_name_len)} | {'Tests'.rjust(6)} | {'Failed'.rjust(6)} | {'Success Rate'.ljust(12)} |" + separator = f"|:{'-' * max_suite_name_len}|{'-' * 7}:|{'-' * 7}:|:{'-' * 11}|" + + table_lines.append(header) + table_lines.append(separator) + + # Add data rows with proper alignment + for suite_name, suite_data in sorted_suites: + stats = suite_data["stats"] + suite_success_rate = f"{(stats['passed'] / stats['tests'] * 100):.2f}%" if stats["tests"] > 0 else "N/A" + + row = f"| {suite_name.ljust(max_suite_name_len)} | {str(stats['tests']).rjust(6)} | {str(stats['failed']).rjust(6)} | {suite_success_rate.ljust(12)} |" + + table_lines.append(row) + + table_lines.append("```") + + # Create the Slack payload with character limit enforcement + payload = [ + {"type": "section", "text": {"type": "mrkdwn", "text": summary}}, + {"type": "section", "text": {"type": "mrkdwn", "text": "\n".join(table_lines)}}, + ] + + # Add action button + if os.environ.get("GITHUB_RUN_ID"): + run_id = os.environ["GITHUB_RUN_ID"] + payload.append( + { + "type": "section", + "text": { + "type": "mrkdwn", + "text": f"**", + }, + } + ) + + # Add date in more compact form + payload.append( + { + "type": "context", + "elements": [ + { + "type": "plain_text", + "text": f"Results for {date.today()}", + }, + ], + } + ) + + # Enforce 3001 character limit + payload_text = str(payload) + if len(payload_text) > MAX_LEN_MESSAGE: + # Truncate table if payload is too long + # Remove rows from the bottom until under limit + original_table_lines = table_lines[:] + while len(str(payload)) > MAX_LEN_MESSAGE and len(table_lines) > 3: # Keep at least header and separator + # Remove the last data row (but keep ``` at the end) + table_lines.pop(-2) # Remove second to last (last is the closing ```) + + # Recreate payload with truncated table + payload[1] = {"type": "section", "text": {"type": "mrkdwn", "text": "\n".join(table_lines)}} + + # Add note if we had to truncate + if len(table_lines) < len(original_table_lines): + truncated_count = len(original_table_lines) - len(table_lines) + table_lines.insert(-1, f"... {truncated_count} more test suites (truncated due to message limit)") + payload[1] = {"type": "section", "text": {"type": "mrkdwn", "text": "\n".join(table_lines)}} + + return payload + + +def create_failed_tests_by_suite_ordered(consolidated_data): + """Group failed tests by test suite, ordered by success rate (ascending).""" + # Sort test suites by success rate (ascending - least successful first) + sorted_suites = sorted( + consolidated_data["test_suites"].items(), + key=lambda x: (x[1]["stats"]["passed"] / x[1]["stats"]["tests"] * 100) if x[1]["stats"]["tests"] > 0 else 0, + reverse=False, + ) + + failed_suite_tests = [] + + # Process suites in order of success rate + for suite_name, suite_data in sorted_suites: + if suite_data["stats"]["failed"] > 0: + suite_failures = [] + + for failure in suite_data.get("failures", []): + test_name = failure["test"] + + # Try to reconstruct full path if partial + if "::" in test_name and "/" in test_name: + full_test_name = test_name + elif "::" in test_name or "." in test_name: + if "/" not in test_name and suite_name not in test_name: + full_test_name = f"{suite_name}::{test_name}" + else: + full_test_name = test_name + else: + full_test_name = f"{suite_name}::{test_name}" + + suite_failures.append(full_test_name) + + # Sort and deduplicate tests within the suite + suite_failures = sorted(set(suite_failures)) + + if suite_failures: + failed_suite_tests.append( + { + "suite_name": suite_name, + "tests": suite_failures, + "success_rate": (suite_data["stats"]["passed"] / suite_data["stats"]["tests"] * 100) + if suite_data["stats"]["tests"] > 0 + else 0, + } + ) + + return failed_suite_tests + + +def main(args): + # Make sure reports directory exists + if not os.path.isdir(args.reports_dir): + print(f"Error: Reports directory '{args.reports_dir}' does not exist.") + return + + # Consolidate reports + consolidated_data = consolidate_reports(args.reports_dir) + + # Check if we found any test results + if consolidated_data["total_stats"]["tests"] == 0: + print(f"Warning: No test results found in '{args.reports_dir}' or its subdirectories.") + + # Generate markdown report + report = generate_report(consolidated_data) + + # Save report to file if specified + if args.output_file: + # Create parent directories if they don't exist + output_dir = os.path.dirname(args.output_file) + if output_dir and not os.path.exists(output_dir): + os.makedirs(output_dir) + + with open(args.output_file, "w") as f: + f.write(report) + + # Only print the report when saving to file + print(report) + + # Send to Slack if token is available (optional, can be disabled) + slack_token = os.environ.get("SLACK_API_TOKEN") + if slack_token and args.slack_channel_name: + payload = create_slack_payload(consolidated_data) + + try: + client = WebClient(token=slack_token) + # Send main message + response = client.chat_postMessage(channel=f"#{args.slack_channel_name}", blocks=payload) + print(f"Report sent to Slack channel: {args.slack_channel_name}") + + # Send failed tests as separate threaded replies grouped by test suite (ordered by success rate) + total = consolidated_data["total_stats"] + if total["failed"] > 0: + failed_suites = create_failed_tests_by_suite_ordered(consolidated_data) + for suite_info in failed_suites: + suite_name = suite_info["suite_name"] + suite_tests = suite_info["tests"] + success_rate = suite_info["success_rate"] + message_text = ( + f"**{suite_name}** (Success Rate: {success_rate:.2f}%)\n```\n" + + "\n".join(suite_tests) + + "\n```" + ) + client.chat_postMessage( + channel=f"#{args.slack_channel_name}", + thread_ts=response["ts"], # Reply in thread + text=message_text, # Use text instead of blocks for markdown + ) + print(f"Failed tests details sent as {len(failed_suites)} thread replies") + except Exception as e: + print(f"Error sending report to Slack: {e}") + + +if __name__ == "__main__": + args = parser.parse_args() + main(args) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/custom_init_isort.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/custom_init_isort.py new file mode 100644 index 0000000000000000000000000000000000000000..2194b5a57063aaf27837774f68d2a9a492c0caef --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/custom_init_isort.py @@ -0,0 +1,330 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Utility that sorts the imports in the custom inits of Diffusers. Diffusers uses init files that delay the +import of an object to when it's actually needed. This is to avoid the main init importing all models, which would +make the line `import transformers` very slow when the user has all optional dependencies installed. The inits with +delayed imports have two halves: one defining a dictionary `_import_structure` which maps modules to the name of the +objects in each module, and one in `TYPE_CHECKING` which looks like a normal init for type-checkers. `isort` or `ruff` +properly sort the second half which looks like traditionl imports, the goal of this script is to sort the first half. + +Use from the root of the repo with: + +```bash +python utils/custom_init_isort.py +``` + +which will auto-sort the imports (used in `make style`). + +For a check only (as used in `make quality`) run: + +```bash +python utils/custom_init_isort.py --check_only +``` +""" + +import argparse +import os +import re +from typing import Any, Callable, List, Optional + + +# Path is defined with the intent you should run this script from the root of the repo. +PATH_TO_TRANSFORMERS = "src/diffusers" + +# Pattern that looks at the indentation in a line. +_re_indent = re.compile(r"^(\s*)\S") +# Pattern that matches `"key":" and puts `key` in group 0. +_re_direct_key = re.compile(r'^\s*"([^"]+)":') +# Pattern that matches `_import_structure["key"]` and puts `key` in group 0. +_re_indirect_key = re.compile(r'^\s*_import_structure\["([^"]+)"\]') +# Pattern that matches `"key",` and puts `key` in group 0. +_re_strip_line = re.compile(r'^\s*"([^"]+)",\s*$') +# Pattern that matches any `[stuff]` and puts `stuff` in group 0. +_re_bracket_content = re.compile(r"\[([^\]]+)\]") + + +def get_indent(line: str) -> str: + """Returns the indent in given line (as string).""" + search = _re_indent.search(line) + return "" if search is None else search.groups()[0] + + +def split_code_in_indented_blocks( + code: str, indent_level: str = "", start_prompt: str | None = None, end_prompt: str | None = None +) -> List[str]: + """ + Split some code into its indented blocks, starting at a given level. + + Args: + code (`str`): The code to split. + indent_level (`str`): The indent level (as string) to use for identifying the blocks to split. + start_prompt (`str`, *optional*): If provided, only starts splitting at the line where this text is. + end_prompt (`str`, *optional*): If provided, stops splitting at a line where this text is. + + Warning: + The text before `start_prompt` or after `end_prompt` (if provided) is not ignored, just not split. The input `code` + can thus be retrieved by joining the result. + + Returns: + `List[str]`: The list of blocks. + """ + # Let's split the code into lines and move to start_index. + index = 0 + lines = code.split("\n") + if start_prompt is not None: + while not lines[index].startswith(start_prompt): + index += 1 + blocks = ["\n".join(lines[:index])] + else: + blocks = [] + + # This variable contains the block treated at a given time. + current_block = [lines[index]] + index += 1 + # We split into blocks until we get to the `end_prompt` (or the end of the file). + while index < len(lines) and (end_prompt is None or not lines[index].startswith(end_prompt)): + # We have a non-empty line with the proper indent -> start of a new block + if len(lines[index]) > 0 and get_indent(lines[index]) == indent_level: + # Store the current block in the result and rest. There are two cases: the line is part of the block (like + # a closing parenthesis) or not. + if len(current_block) > 0 and get_indent(current_block[-1]).startswith(indent_level + " "): + # Line is part of the current block + current_block.append(lines[index]) + blocks.append("\n".join(current_block)) + if index < len(lines) - 1: + current_block = [lines[index + 1]] + index += 1 + else: + current_block = [] + else: + # Line is not part of the current block + blocks.append("\n".join(current_block)) + current_block = [lines[index]] + else: + # Just add the line to the current block + current_block.append(lines[index]) + index += 1 + + # Adds current block if it's nonempty. + if len(current_block) > 0: + blocks.append("\n".join(current_block)) + + # Add final block after end_prompt if provided. + if end_prompt is not None and index < len(lines): + blocks.append("\n".join(lines[index:])) + + return blocks + + +def ignore_underscore_and_lowercase(key: Callable[[Any], str]) -> Callable[[Any], str]: + """ + Wraps a key function (as used in a sort) to lowercase and ignore underscores. + """ + + def _inner(x): + return key(x).lower().replace("_", "") + + return _inner + + +def sort_objects(objects: List[Any], key: Optional[Callable[[Any], str]] = None) -> List[Any]: + """ + Sort a list of objects following the rules of isort (all uppercased first, camel-cased second and lower-cased + last). + + Args: + objects (`List[Any]`): + The list of objects to sort. + key (`Callable[[Any], str]`, *optional*): + A function taking an object as input and returning a string, used to sort them by alphabetical order. + If not provided, will default to noop (so a `key` must be provided if the `objects` are not of type string). + + Returns: + `List[Any]`: The sorted list with the same elements as in the inputs + """ + + # If no key is provided, we use a noop. + def noop(x): + return x + + if key is None: + key = noop + # Constants are all uppercase, they go first. + constants = [obj for obj in objects if key(obj).isupper()] + # Classes are not all uppercase but start with a capital, they go second. + classes = [obj for obj in objects if key(obj)[0].isupper() and not key(obj).isupper()] + # Functions begin with a lowercase, they go last. + functions = [obj for obj in objects if not key(obj)[0].isupper()] + + # Then we sort each group. + key1 = ignore_underscore_and_lowercase(key) + return sorted(constants, key=key1) + sorted(classes, key=key1) + sorted(functions, key=key1) + + +def sort_objects_in_import(import_statement: str) -> str: + """ + Sorts the imports in a single import statement. + + Args: + import_statement (`str`): The import statement in which to sort the imports. + + Returns: + `str`: The same as the input, but with objects properly sorted. + """ + + # This inner function sort imports between [ ]. + def _replace(match): + imports = match.groups()[0] + # If there is one import only, nothing to do. + if "," not in imports: + return f"[{imports}]" + keys = [part.strip().replace('"', "") for part in imports.split(",")] + # We will have a final empty element if the line finished with a comma. + if len(keys[-1]) == 0: + keys = keys[:-1] + return "[" + ", ".join([f'"{k}"' for k in sort_objects(keys)]) + "]" + + lines = import_statement.split("\n") + if len(lines) > 3: + # Here we have to sort internal imports that are on several lines (one per name): + # key: [ + # "object1", + # "object2", + # ... + # ] + + # We may have to ignore one or two lines on each side. + idx = 2 if lines[1].strip() == "[" else 1 + keys_to_sort = [(i, _re_strip_line.search(line).groups()[0]) for i, line in enumerate(lines[idx:-idx])] + sorted_indices = sort_objects(keys_to_sort, key=lambda x: x[1]) + sorted_lines = [lines[x[0] + idx] for x in sorted_indices] + return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]) + elif len(lines) == 3: + # Here we have to sort internal imports that are on one separate line: + # key: [ + # "object1", "object2", ... + # ] + if _re_bracket_content.search(lines[1]) is not None: + lines[1] = _re_bracket_content.sub(_replace, lines[1]) + else: + keys = [part.strip().replace('"', "") for part in lines[1].split(",")] + # We will have a final empty element if the line finished with a comma. + if len(keys[-1]) == 0: + keys = keys[:-1] + lines[1] = get_indent(lines[1]) + ", ".join([f'"{k}"' for k in sort_objects(keys)]) + return "\n".join(lines) + else: + # Finally we have to deal with imports fitting on one line + import_statement = _re_bracket_content.sub(_replace, import_statement) + return import_statement + + +def sort_imports(file: str, check_only: bool = True): + """ + Sort the imports defined in the `_import_structure` of a given init. + + Args: + file (`str`): The path to the init to check/fix. + check_only (`bool`, *optional*, defaults to `True`): Whether or not to just check (and not auto-fix) the init. + """ + with open(file, encoding="utf-8") as f: + code = f.read() + + # If the file is not a custom init, there is nothing to do. + if "_import_structure" not in code: + return + + # Blocks of indent level 0 + main_blocks = split_code_in_indented_blocks( + code, start_prompt="_import_structure = {", end_prompt="if TYPE_CHECKING:" + ) + + # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt). + for block_idx in range(1, len(main_blocks) - 1): + # Check if the block contains some `_import_structure`s thingy to sort. + block = main_blocks[block_idx] + block_lines = block.split("\n") + + # Get to the start of the imports. + line_idx = 0 + while line_idx < len(block_lines) and "_import_structure" not in block_lines[line_idx]: + # Skip dummy import blocks + if "import dummy" in block_lines[line_idx]: + line_idx = len(block_lines) + else: + line_idx += 1 + if line_idx >= len(block_lines): + continue + + # Ignore beginning and last line: they don't contain anything. + internal_block_code = "\n".join(block_lines[line_idx:-1]) + indent = get_indent(block_lines[1]) + # Slit the internal block into blocks of indent level 1. + internal_blocks = split_code_in_indented_blocks(internal_block_code, indent_level=indent) + # We have two categories of import key: list or _import_structure[key].append/extend + pattern = _re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key + # Grab the keys, but there is a trap: some lines are empty or just comments. + keys = [(pattern.search(b).groups()[0] if pattern.search(b) is not None else None) for b in internal_blocks] + # We only sort the lines with a key. + keys_to_sort = [(i, key) for i, key in enumerate(keys) if key is not None] + sorted_indices = [x[0] for x in sorted(keys_to_sort, key=lambda x: x[1])] + + # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. + count = 0 + reordered_blocks = [] + for i in range(len(internal_blocks)): + if keys[i] is None: + reordered_blocks.append(internal_blocks[i]) + else: + block = sort_objects_in_import(internal_blocks[sorted_indices[count]]) + reordered_blocks.append(block) + count += 1 + + # And we put our main block back together with its first and last line. + main_blocks[block_idx] = "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]) + + if code != "\n".join(main_blocks): + if check_only: + return True + else: + print(f"Overwriting {file}.") + with open(file, "w", encoding="utf-8") as f: + f.write("\n".join(main_blocks)) + + +def sort_imports_in_all_inits(check_only=True): + """ + Sort the imports defined in the `_import_structure` of all inits in the repo. + + Args: + check_only (`bool`, *optional*, defaults to `True`): Whether or not to just check (and not auto-fix) the init. + """ + failures = [] + for root, _, files in os.walk(PATH_TO_TRANSFORMERS): + if "__init__.py" in files: + result = sort_imports(os.path.join(root, "__init__.py"), check_only=check_only) + if result: + failures = [os.path.join(root, "__init__.py")] + if len(failures) > 0: + raise ValueError(f"Would overwrite {len(failures)} files, run `make style`.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") + args = parser.parse_args() + + sort_imports_in_all_inits(check_only=args.check_only) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/extract_tests_from_mixin.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/extract_tests_from_mixin.py new file mode 100644 index 0000000000000000000000000000000000000000..c8b65b96ee16071042d93b66071367acad658d9c --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/extract_tests_from_mixin.py @@ -0,0 +1,61 @@ +import argparse +import inspect +import sys +from pathlib import Path +from typing import List, Type + + +root_dir = Path(__file__).parent.parent.absolute() +sys.path.insert(0, str(root_dir)) + +parser = argparse.ArgumentParser() +parser.add_argument("--type", type=str, default=None) +args = parser.parse_args() + + +def get_test_methods_from_class(cls: Type) -> List[str]: + """ + Get all test method names from a given class. + Only returns methods that start with 'test_'. + """ + test_methods = [] + for name, obj in inspect.getmembers(cls): + if name.startswith("test_") and inspect.isfunction(obj): + test_methods.append(name) + return sorted(test_methods) + + +def generate_pytest_pattern(test_methods: List[str]) -> str: + """Generate pytest pattern string for the -k flag.""" + return " or ".join(test_methods) + + +def generate_pattern_for_mixin(mixin_class: Type) -> str: + """ + Generate pytest pattern for a specific mixin class. + """ + if mixin_cls is None: + return "" + test_methods = get_test_methods_from_class(mixin_class) + return generate_pytest_pattern(test_methods) + + +if __name__ == "__main__": + mixin_cls = None + if args.type == "pipeline": + from tests.pipelines.test_pipelines_common import PipelineTesterMixin + + mixin_cls = PipelineTesterMixin + + elif args.type == "models": + from tests.models.test_modeling_common import ModelTesterMixin + + mixin_cls = ModelTesterMixin + + elif args.type == "lora": + from tests.lora.utils import PeftLoraLoaderMixinTests + + mixin_cls = PeftLoraLoaderMixinTests + + pattern = generate_pattern_for_mixin(mixin_cls) + print(pattern) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/fetch_latest_release_branch.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/fetch_latest_release_branch.py new file mode 100644 index 0000000000000000000000000000000000000000..5b0be6253e1b96af3883da916fc06c5522321dcf --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/fetch_latest_release_branch.py @@ -0,0 +1,73 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import requests +from packaging.version import parse + + +# GitHub repository details +USER = "huggingface" +REPO = "diffusers" + + +def fetch_all_branches(user, repo): + branches = [] # List to store all branches + page = 1 # Start from first page + while True: + # Make a request to the GitHub API for the branches + response = requests.get( + f"https://api.github.com/repos/{user}/{repo}/branches", + params={"page": page}, + timeout=60, + ) + + # Check if the request was successful + if response.status_code == 200: + # Add the branches from the current page to the list + branches.extend([branch["name"] for branch in response.json()]) + + # Check if there is a 'next' link for pagination + if "next" in response.links: + page += 1 # Move to the next page + else: + break # Exit loop if there is no next page + else: + print("Failed to retrieve branches:", response.status_code) + break + + return branches + + +def main(): + # Fetch all branches + branches = fetch_all_branches(USER, REPO) + + # Filter branches. + # print(f"Total branches: {len(branches)}") + filtered_branches = [] + for branch in branches: + if branch.startswith("v") and ("-release" in branch or "-patch" in branch): + filtered_branches.append(branch) + # print(f"Filtered: {branch}") + + sorted_branches = sorted(filtered_branches, key=lambda x: parse(x.split("-")[0][1:]), reverse=True) + latest_branch = sorted_branches[0] + # print(f"Latest branch: {latest_branch}") + return latest_branch + + +if __name__ == "__main__": + print(main()) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/fetch_torch_cuda_pipeline_test_matrix.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/fetch_torch_cuda_pipeline_test_matrix.py new file mode 100644 index 0000000000000000000000000000000000000000..86f3c4bf5f3737f186a6a13f0d43e38235349d32 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/fetch_torch_cuda_pipeline_test_matrix.py @@ -0,0 +1,99 @@ +import json +import logging +import os +from collections import defaultdict +from pathlib import Path + +from huggingface_hub import HfApi + +import diffusers + + +PATH_TO_REPO = Path(__file__).parent.parent.resolve() +ALWAYS_TEST_PIPELINE_MODULES = [ + "controlnet", + "controlnet_flux", + "controlnet_sd3", + "stable_diffusion", + "stable_diffusion_2", + "stable_diffusion_3", + "stable_diffusion_xl", + "ip_adapters", + "flux", +] +PIPELINE_USAGE_CUTOFF = int(os.getenv("PIPELINE_USAGE_CUTOFF", 50000)) + +logger = logging.getLogger(__name__) +api = HfApi() + + +def filter_pipelines(usage_dict, usage_cutoff=10000): + output = [] + for diffusers_object, usage in usage_dict.items(): + if usage < usage_cutoff: + continue + + is_diffusers_pipeline = hasattr(diffusers.pipelines, diffusers_object) + if not is_diffusers_pipeline: + continue + + output.append(diffusers_object) + + return output + + +def fetch_pipeline_objects(): + models = api.list_models(filter="diffusers") + downloads = defaultdict(int) + + for model in models: + is_counted = False + for tag in model.tags: + if tag.startswith("diffusers:"): + is_counted = True + downloads[tag[len("diffusers:") :]] += model.downloads + + if not is_counted: + downloads["other"] += model.downloads + + # Remove 0 downloads + downloads = {k: v for k, v in downloads.items() if v > 0} + pipeline_objects = filter_pipelines(downloads, PIPELINE_USAGE_CUTOFF) + + return pipeline_objects + + +def fetch_pipeline_modules_to_test(): + try: + pipeline_objects = fetch_pipeline_objects() + except Exception as e: + logger.error(e) + raise RuntimeError("Unable to fetch model list from HuggingFace Hub.") + + test_modules = [] + for pipeline_name in pipeline_objects: + module = getattr(diffusers, pipeline_name) + + test_module = module.__module__.split(".")[-2].strip() + test_modules.append(test_module) + + return test_modules + + +def main(): + test_modules = fetch_pipeline_modules_to_test() + test_modules.extend(ALWAYS_TEST_PIPELINE_MODULES) + + # Get unique modules + test_modules = sorted(set(test_modules)) + print(json.dumps(test_modules)) + + save_path = f"{PATH_TO_REPO}/reports" + os.makedirs(save_path, exist_ok=True) + + with open(f"{save_path}/test-pipelines.json", "w") as f: + json.dump({"pipeline_test_modules": test_modules}, f) + + +if __name__ == "__main__": + main() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/generate_model_tests.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/generate_model_tests.py new file mode 100644 index 0000000000000000000000000000000000000000..11acd2175e21067906718e47fdb24edf5f49a73e --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/generate_model_tests.py @@ -0,0 +1,592 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2025 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Utility script to generate test suites for diffusers model classes. + +Usage: + python utils/generate_model_tests.py src/diffusers/models/transformers/transformer_flux.py + +This will analyze the model file and generate a test file with appropriate +test classes based on the model's mixins and attributes. +""" + +import argparse +import ast +import sys +from pathlib import Path + + +MIXIN_TO_TESTER = { + "ModelMixin": "ModelTesterMixin", + "PeftAdapterMixin": "LoraTesterMixin", +} + +ATTRIBUTE_TO_TESTER = { + "_cp_plan": "ContextParallelTesterMixin", + "_supports_gradient_checkpointing": "TrainingTesterMixin", +} + +ALWAYS_INCLUDE_TESTERS = [ + "ModelTesterMixin", + "MemoryTesterMixin", + "TorchCompileTesterMixin", +] + +# Attention-related class names that indicate the model uses attention +ATTENTION_INDICATORS = { + "AttentionMixin", + "AttentionModuleMixin", +} + +OPTIONAL_TESTERS = [ + # Quantization testers + ("BitsAndBytesTesterMixin", "bnb"), + ("QuantoTesterMixin", "quanto"), + ("TorchAoTesterMixin", "torchao"), + ("GGUFTesterMixin", "gguf"), + ("ModelOptTesterMixin", "modelopt"), + # Quantization compile testers + ("BitsAndBytesCompileTesterMixin", "bnb_compile"), + ("QuantoCompileTesterMixin", "quanto_compile"), + ("TorchAoCompileTesterMixin", "torchao_compile"), + ("GGUFCompileTesterMixin", "gguf_compile"), + ("ModelOptCompileTesterMixin", "modelopt_compile"), + # Cache testers + ("PyramidAttentionBroadcastTesterMixin", "pab_cache"), + ("FirstBlockCacheTesterMixin", "fbc_cache"), + ("FasterCacheTesterMixin", "faster_cache"), + # Other testers + ("SingleFileTesterMixin", "single_file"), + ("IPAdapterTesterMixin", "ip_adapter"), +] + + +class ModelAnalyzer(ast.NodeVisitor): + def __init__(self): + self.model_classes = [] + self.current_class = None + self.imports = set() + + def visit_Import(self, node: ast.Import): + for alias in node.names: + self.imports.add(alias.name.split(".")[-1]) + self.generic_visit(node) + + def visit_ImportFrom(self, node: ast.ImportFrom): + for alias in node.names: + self.imports.add(alias.name) + self.generic_visit(node) + + def visit_ClassDef(self, node: ast.ClassDef): + base_names = [] + for base in node.bases: + if isinstance(base, ast.Name): + base_names.append(base.id) + elif isinstance(base, ast.Attribute): + base_names.append(base.attr) + + if "ModelMixin" in base_names: + class_info = { + "name": node.name, + "bases": base_names, + "attributes": {}, + "has_forward": False, + "init_params": [], + } + + for item in node.body: + if isinstance(item, ast.Assign): + for target in item.targets: + if isinstance(target, ast.Name): + attr_name = target.id + if attr_name.startswith("_"): + class_info["attributes"][attr_name] = self._get_value(item.value) + + elif isinstance(item, ast.FunctionDef): + if item.name == "forward": + class_info["has_forward"] = True + class_info["forward_params"] = self._extract_func_params(item) + elif item.name == "__init__": + class_info["init_params"] = self._extract_func_params(item) + + self.model_classes.append(class_info) + + self.generic_visit(node) + + def _extract_func_params(self, func_node: ast.FunctionDef) -> list[dict]: + params = [] + args = func_node.args + + num_defaults = len(args.defaults) + num_args = len(args.args) + first_default_idx = num_args - num_defaults + + for i, arg in enumerate(args.args): + if arg.arg == "self": + continue + + param_info = {"name": arg.arg, "type": None, "default": None} + + if arg.annotation: + param_info["type"] = self._get_annotation_str(arg.annotation) + + default_idx = i - first_default_idx + if default_idx >= 0 and default_idx < len(args.defaults): + param_info["default"] = self._get_value(args.defaults[default_idx]) + + params.append(param_info) + + return params + + def _get_annotation_str(self, node) -> str: + if isinstance(node, ast.Name): + return node.id + elif isinstance(node, ast.Constant): + return repr(node.value) + elif isinstance(node, ast.Subscript): + base = self._get_annotation_str(node.value) + if isinstance(node.slice, ast.Tuple): + args = ", ".join(self._get_annotation_str(el) for el in node.slice.elts) + else: + args = self._get_annotation_str(node.slice) + return f"{base}[{args}]" + elif isinstance(node, ast.Attribute): + return f"{self._get_annotation_str(node.value)}.{node.attr}" + elif isinstance(node, ast.BinOp) and isinstance(node.op, ast.BitOr): + left = self._get_annotation_str(node.left) + right = self._get_annotation_str(node.right) + return f"{left} | {right}" + elif isinstance(node, ast.Tuple): + return ", ".join(self._get_annotation_str(el) for el in node.elts) + return "Any" + + def _get_value(self, node): + if isinstance(node, ast.Constant): + return node.value + elif isinstance(node, ast.Name): + if node.id == "None": + return None + elif node.id == "True": + return True + elif node.id == "False": + return False + return node.id + elif isinstance(node, ast.List): + return [self._get_value(el) for el in node.elts] + elif isinstance(node, ast.Dict): + return {self._get_value(k): self._get_value(v) for k, v in zip(node.keys, node.values)} + return "" + + +def analyze_model_file(filepath: str) -> tuple[list[dict], set[str]]: + with open(filepath) as f: + source = f.read() + + tree = ast.parse(source) + analyzer = ModelAnalyzer() + analyzer.visit(tree) + + return analyzer.model_classes, analyzer.imports + + +def determine_testers(model_info: dict, include_optional: list[str], imports: set[str]) -> list[str]: + testers = list(ALWAYS_INCLUDE_TESTERS) + + for base in model_info["bases"]: + if base in MIXIN_TO_TESTER: + tester = MIXIN_TO_TESTER[base] + if tester not in testers: + testers.append(tester) + + for attr, tester in ATTRIBUTE_TO_TESTER.items(): + if attr in model_info["attributes"]: + value = model_info["attributes"][attr] + if value is not None and value is not False: + if tester not in testers: + testers.append(tester) + + if "_cp_plan" in model_info["attributes"] and model_info["attributes"]["_cp_plan"] is not None: + if "ContextParallelTesterMixin" not in testers: + testers.append("ContextParallelTesterMixin") + + # Include AttentionTesterMixin if the model imports attention-related classes + if imports & ATTENTION_INDICATORS: + testers.append("AttentionTesterMixin") + + for tester, flag in OPTIONAL_TESTERS: + if flag in include_optional: + if tester not in testers: + testers.append(tester) + + return testers + + +def generate_config_class(model_info: dict, model_name: str) -> str: + class_name = f"{model_name}TesterConfig" + model_class = model_info["name"] + forward_params = model_info.get("forward_params", []) + init_params = model_info.get("init_params", []) + + lines = [ + f"class {class_name}:", + " @property", + " def model_class(self):", + f" return {model_class}", + "", + " @property", + " def pretrained_model_name_or_path(self):", + ' return "" # TODO: Set Hub repository ID', + "", + " @property", + " def pretrained_model_kwargs(self):", + ' return {"subfolder": "transformer"}', + "", + " @property", + " def generator(self):", + ' return torch.Generator("cpu").manual_seed(0)', + "", + " def get_init_dict(self) -> dict[str, int | list[int]]:", + ] + + if init_params: + lines.append(" # __init__ parameters:") + for param in init_params: + type_str = f": {param['type']}" if param["type"] else "" + default_str = f" = {param['default']}" if param["default"] is not None else "" + lines.append(f" # {param['name']}{type_str}{default_str}") + + lines.extend( + [ + " return {}", + "", + " def get_dummy_inputs(self) -> dict[str, torch.Tensor]:", + ] + ) + + if forward_params: + lines.append(" # forward() parameters:") + for param in forward_params: + type_str = f": {param['type']}" if param["type"] else "" + default_str = f" = {param['default']}" if param["default"] is not None else "" + lines.append(f" # {param['name']}{type_str}{default_str}") + + lines.extend( + [ + " # TODO: Fill in dummy inputs", + " return {}", + "", + " @property", + " def input_shape(self) -> tuple[int, ...]:", + " return (1, 1)", + "", + " @property", + " def output_shape(self) -> tuple[int, ...]:", + " return (1, 1)", + ] + ) + + return "\n".join(lines) + + +def generate_test_class(model_name: str, config_class: str, tester: str) -> str: + tester_short = tester.replace("TesterMixin", "") + class_name = f"Test{model_name}{tester_short}" + + lines = [f"class {class_name}({config_class}, {tester}):"] + + if tester == "TorchCompileTesterMixin": + lines.extend( + [ + " @property", + " def different_shapes_for_compilation(self):", + " return [(4, 4), (4, 8), (8, 8)]", + "", + " def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:", + " # TODO: Implement dynamic input generation", + " return {}", + ] + ) + elif tester == "IPAdapterTesterMixin": + lines.extend( + [ + " @property", + " def ip_adapter_processor_cls(self):", + " return None # TODO: Set processor class", + "", + " def modify_inputs_for_ip_adapter(self, model, inputs_dict):", + " # TODO: Add IP adapter image embeds to inputs", + " return inputs_dict", + "", + " def create_ip_adapter_state_dict(self, model):", + " # TODO: Create IP adapter state dict", + " return {}", + ] + ) + elif tester == "SingleFileTesterMixin": + lines.extend( + [ + " @property", + " def ckpt_path(self):", + ' return "" # TODO: Set checkpoint path', + "", + " @property", + " def alternate_ckpt_paths(self):", + " return []", + "", + " @property", + " def pretrained_model_name_or_path(self):", + ' return "" # TODO: Set Hub repository ID', + ] + ) + elif tester == "GGUFTesterMixin": + lines.extend( + [ + " @property", + " def gguf_filename(self):", + ' return "" # TODO: Set GGUF filename', + "", + " def get_dummy_inputs(self) -> dict[str, torch.Tensor]:", + " # TODO: Override with larger inputs for quantization tests", + " return {}", + ] + ) + elif tester in ["BitsAndBytesTesterMixin", "QuantoTesterMixin", "TorchAoTesterMixin", "ModelOptTesterMixin"]: + lines.extend( + [ + " def get_dummy_inputs(self) -> dict[str, torch.Tensor]:", + " # TODO: Override with larger inputs for quantization tests", + " return {}", + ] + ) + elif tester in [ + "BitsAndBytesCompileTesterMixin", + "QuantoCompileTesterMixin", + "TorchAoCompileTesterMixin", + "ModelOptCompileTesterMixin", + ]: + lines.extend( + [ + " def get_dummy_inputs(self) -> dict[str, torch.Tensor]:", + " # TODO: Override with larger inputs for quantization compile tests", + " return {}", + ] + ) + elif tester == "GGUFCompileTesterMixin": + lines.extend( + [ + " @property", + " def gguf_filename(self):", + ' return "" # TODO: Set GGUF filename', + "", + " def get_dummy_inputs(self) -> dict[str, torch.Tensor]:", + " # TODO: Override with larger inputs for quantization compile tests", + " return {}", + ] + ) + elif tester in [ + "PyramidAttentionBroadcastTesterMixin", + "FirstBlockCacheTesterMixin", + "FasterCacheTesterMixin", + ]: + lines.append(" pass") + elif tester == "LoraHotSwappingForModelTesterMixin": + lines.extend( + [ + " @property", + " def different_shapes_for_compilation(self):", + " return [(4, 4), (4, 8), (8, 8)]", + "", + " def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:", + " # TODO: Implement dynamic input generation", + " return {}", + ] + ) + else: + lines.append(" pass") + + return "\n".join(lines) + + +def generate_test_file(model_info: dict, model_filepath: str, include_optional: list[str], imports: set[str]) -> str: + model_name = model_info["name"].replace("2DModel", "").replace("3DModel", "").replace("Model", "") + testers = determine_testers(model_info, include_optional, imports) + tester_imports = sorted(set(testers) - {"LoraHotSwappingForModelTesterMixin"}) + + lines = [ + "# coding=utf-8", + "# Copyright 2025 HuggingFace Inc.", + "#", + '# Licensed under the Apache License, Version 2.0 (the "License");', + "# you may not use this file except in compliance with the License.", + "# You may obtain a copy of the License at", + "#", + "# http://www.apache.org/licenses/LICENSE-2.0", + "#", + "# Unless required by applicable law or agreed to in writing, software", + '# distributed under the License is distributed on an "AS IS" BASIS,', + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.", + "# See the License for the specific language governing permissions and", + "# limitations under the License.", + "", + "import torch", + "", + f"from diffusers import {model_info['name']}", + "from diffusers.utils.torch_utils import randn_tensor", + "", + "from ...testing_utils import enable_full_determinism, torch_device", + ] + + if "LoraTesterMixin" in testers: + lines.append("from ..test_modeling_common import LoraHotSwappingForModelTesterMixin") + + lines.extend( + [ + "from ..testing_utils import (", + *[f" {tester}," for tester in sorted(tester_imports)], + ")", + "", + "", + "enable_full_determinism()", + "", + "", + ] + ) + + config_class = f"{model_name}TesterConfig" + lines.append(generate_config_class(model_info, model_name)) + lines.append("") + lines.append("") + + for tester in testers: + lines.append(generate_test_class(model_name, config_class, tester)) + lines.append("") + lines.append("") + + if "LoraTesterMixin" in testers: + lines.append(generate_test_class(model_name, config_class, "LoraHotSwappingForModelTesterMixin")) + lines.append("") + lines.append("") + + return "\n".join(lines).rstrip() + "\n" + + +def get_test_output_path(model_filepath: str) -> str: + path = Path(model_filepath) + model_filename = path.stem + + if "transformers" in path.parts: + return f"tests/models/transformers/test_models_{model_filename}.py" + elif "unets" in path.parts: + return f"tests/models/unets/test_models_{model_filename}.py" + elif "autoencoders" in path.parts: + return f"tests/models/autoencoders/test_models_{model_filename}.py" + else: + return f"tests/models/test_models_{model_filename}.py" + + +def main(): + parser = argparse.ArgumentParser(description="Generate test suite for a diffusers model class") + parser.add_argument( + "model_filepath", + type=str, + help="Path to the model file (e.g., src/diffusers/models/transformers/transformer_flux.py)", + ) + parser.add_argument( + "--output", "-o", type=str, default=None, help="Output file path (default: auto-generated based on model path)" + ) + parser.add_argument( + "--include", + "-i", + type=str, + nargs="*", + default=[], + choices=[ + "bnb", + "quanto", + "torchao", + "gguf", + "modelopt", + "bnb_compile", + "quanto_compile", + "torchao_compile", + "gguf_compile", + "modelopt_compile", + "pab_cache", + "fbc_cache", + "faster_cache", + "single_file", + "ip_adapter", + "all", + ], + help="Optional testers to include", + ) + parser.add_argument( + "--class-name", + "-c", + type=str, + default=None, + help="Specific model class to generate tests for (default: first model class found)", + ) + parser.add_argument("--dry-run", action="store_true", help="Print generated code without writing to file") + + args = parser.parse_args() + + if not Path(args.model_filepath).exists(): + print(f"Error: File not found: {args.model_filepath}", file=sys.stderr) + sys.exit(1) + + model_classes, imports = analyze_model_file(args.model_filepath) + + if not model_classes: + print(f"Error: No model classes found in {args.model_filepath}", file=sys.stderr) + sys.exit(1) + + if args.class_name: + model_info = next((m for m in model_classes if m["name"] == args.class_name), None) + if not model_info: + available = [m["name"] for m in model_classes] + print(f"Error: Class '{args.class_name}' not found. Available: {available}", file=sys.stderr) + sys.exit(1) + else: + model_info = model_classes[0] + if len(model_classes) > 1: + print(f"Multiple model classes found, using: {model_info['name']}", file=sys.stderr) + print("Use --class-name to specify a different class", file=sys.stderr) + + include_optional = args.include + if "all" in include_optional: + include_optional = [flag for _, flag in OPTIONAL_TESTERS] + + generated_code = generate_test_file(model_info, args.model_filepath, include_optional, imports) + + if args.dry_run: + print(generated_code) + else: + output_path = args.output or get_test_output_path(args.model_filepath) + output_dir = Path(output_path).parent + output_dir.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w") as f: + f.write(generated_code) + + print(f"Generated test file: {output_path}") + print(f"Model class: {model_info['name']}") + print(f"Detected attributes: {list(model_info['attributes'].keys())}") + + +if __name__ == "__main__": + main() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/get_modified_files.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/get_modified_files.py new file mode 100644 index 0000000000000000000000000000000000000000..e392e50c12d3af270df748987be33ee6c3937901 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/get_modified_files.py @@ -0,0 +1,34 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.: +# python ./utils/get_modified_files.py utils src tests examples +# +# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered +# since the output of this script is fed into Makefile commands it doesn't print a newline after the results + +import re +import subprocess +import sys + + +fork_point_sha = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8") +modified_files = subprocess.check_output(f"git diff --name-only {fork_point_sha}".split()).decode("utf-8").split() + +joined_dirs = "|".join(sys.argv[1:]) +regex = re.compile(rf"^({joined_dirs}).*?\.py$") + +relevant_modified_files = [x for x in modified_files if regex.match(x)] +print(" ".join(relevant_modified_files), end="") diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/log_reports.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/log_reports.py new file mode 100644 index 0000000000000000000000000000000000000000..5575c9ba8415a73358fe48e3d0ac41b2188691d5 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/log_reports.py @@ -0,0 +1,139 @@ +import argparse +import json +import os +from datetime import date +from pathlib import Path + +from slack_sdk import WebClient +from tabulate import tabulate + + +MAX_LEN_MESSAGE = 2900 # slack endpoint has a limit of 3001 characters + +parser = argparse.ArgumentParser() +parser.add_argument("--slack_channel_name", default="diffusers-ci-nightly") + + +def main(slack_channel_name=None): + failed = [] + passed = [] + + group_info = [] + + total_num_failed = 0 + empty_file = False or len(list(Path().glob("*.log"))) == 0 + + total_empty_files = [] + + for log in Path().glob("*.log"): + section_num_failed = 0 + i = 0 + with open(log) as f: + for line in f: + line = json.loads(line) + i += 1 + if line.get("nodeid", "") != "": + test = line["nodeid"] + if line.get("duration", None) is not None: + duration = f"{line['duration']:.4f}" + if line.get("outcome", "") == "failed": + section_num_failed += 1 + failed.append([test, duration, log.name.split("_")[0]]) + total_num_failed += 1 + else: + passed.append([test, duration, log.name.split("_")[0]]) + empty_file = i == 0 + group_info.append([str(log), section_num_failed, failed]) + total_empty_files.append(empty_file) + os.remove(log) + failed = [] + text = ( + "🌞 There were no failures!" + if not any(total_empty_files) + else "Something went wrong there is at least one empty file - please check GH action results." + ) + no_error_payload = { + "type": "section", + "text": { + "type": "plain_text", + "text": text, + "emoji": True, + }, + } + + message = "" + payload = [ + { + "type": "header", + "text": { + "type": "plain_text", + "text": "🤗 Results of the Diffusers scheduled nightly tests.", + }, + }, + ] + if total_num_failed > 0: + for i, (name, num_failed, failed_tests) in enumerate(group_info): + if num_failed > 0: + if num_failed == 1: + message += f"*{name}: {num_failed} failed test*\n" + else: + message += f"*{name}: {num_failed} failed tests*\n" + failed_table = [] + for test in failed_tests: + failed_table.append(test[0].split("::")) + failed_table = tabulate( + failed_table, + headers=["Test Location", "Test Case", "Test Name"], + showindex="always", + tablefmt="grid", + maxcolwidths=[12, 12, 12], + ) + message += "\n```\n" + failed_table + "\n```" + + if total_empty_files[i]: + message += f"\n*{name}: Warning! Empty file - please check the GitHub action job *\n" + print(f"### {message}") + else: + payload.append(no_error_payload) + + if len(message) > MAX_LEN_MESSAGE: + print(f"Truncating long message from {len(message)} to {MAX_LEN_MESSAGE}") + message = message[:MAX_LEN_MESSAGE] + "..." + + if len(message) != 0: + md_report = { + "type": "section", + "text": {"type": "mrkdwn", "text": message}, + } + payload.append(md_report) + action_button = { + "type": "section", + "text": {"type": "mrkdwn", "text": "*For more details:*"}, + "accessory": { + "type": "button", + "text": {"type": "plain_text", "text": "Check Action results", "emoji": True}, + "url": f"https://github.com/huggingface/diffusers/actions/runs/{os.environ['GITHUB_RUN_ID']}", + }, + } + payload.append(action_button) + + date_report = { + "type": "context", + "elements": [ + { + "type": "plain_text", + "text": f"Nightly test results for {date.today()}", + }, + ], + } + payload.append(date_report) + + print(payload) + + client = WebClient(token=os.environ.get("SLACK_API_TOKEN")) + client.chat_postMessage(channel=f"#{slack_channel_name}", text=message, blocks=payload) + + +if __name__ == "__main__": + args = parser.parse_args() + main(args.slack_channel_name) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/modular_auto_docstring.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/modular_auto_docstring.py new file mode 100644 index 0000000000000000000000000000000000000000..fc4a82f98ea148589e2a78be780dd413f61eebe6 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/modular_auto_docstring.py @@ -0,0 +1,352 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Auto Docstring Generator for Modular Pipeline Blocks + +This script scans Python files for classes that have `# auto_docstring` comment above them +and inserts/updates the docstring from the class's `doc` property. + +Run from the root of the repo: + python utils/modular_auto_docstring.py [path] [--fix_and_overwrite] + +Examples: + # Check for auto_docstring markers (will error if found without proper docstring) + python utils/modular_auto_docstring.py + + # Check specific directory + python utils/modular_auto_docstring.py src/diffusers/modular_pipelines/ + + # Fix and overwrite the docstrings + python utils/modular_auto_docstring.py --fix_and_overwrite + +Usage in code: + # auto_docstring + class QwenImageAutoVaeEncoderStep(AutoPipelineBlocks): + # docstring will be automatically inserted here + + @property + def doc(self): + return "Your docstring content..." +""" + +import argparse +import ast +import glob +import importlib +import os +import re +import subprocess +import sys + + +# All paths are set with the intent you should run this script from the root of the repo +DIFFUSERS_PATH = "src/diffusers" +REPO_PATH = "." + +# Pattern to match the auto_docstring comment +AUTO_DOCSTRING_PATTERN = re.compile(r"^\s*#\s*auto_docstring\s*$") + + +def setup_diffusers_import(): + """Setup import path to use the local diffusers module.""" + src_path = os.path.join(REPO_PATH, "src") + if src_path not in sys.path: + sys.path.insert(0, src_path) + + +def get_module_from_filepath(filepath: str) -> str: + """Convert a filepath to a module name.""" + filepath = os.path.normpath(filepath) + + if filepath.startswith("src" + os.sep): + filepath = filepath[4:] + + if filepath.endswith(".py"): + filepath = filepath[:-3] + + module_name = filepath.replace(os.sep, ".") + return module_name + + +def load_module(filepath: str): + """Load a module from filepath.""" + setup_diffusers_import() + module_name = get_module_from_filepath(filepath) + + try: + module = importlib.import_module(module_name) + return module + except Exception as e: + print(f"Warning: Could not import module {module_name}: {e}") + return None + + +def get_doc_from_class(module, class_name: str) -> str: + """Get the doc property from an instantiated class.""" + if module is None: + return None + + cls = getattr(module, class_name, None) + if cls is None: + return None + + try: + instance = cls() + if hasattr(instance, "doc"): + return instance.doc + except Exception as e: + print(f"Warning: Could not instantiate {class_name}: {e}") + + return None + + +def find_auto_docstring_classes(filepath: str) -> list: + """ + Find all classes in a file that have # auto_docstring comment above them. + + Returns list of (class_name, class_line_number, has_existing_docstring, docstring_end_line) + """ + with open(filepath, "r", encoding="utf-8", newline="\n") as f: + lines = f.readlines() + + # Parse AST to find class locations and their docstrings + content = "".join(lines) + try: + tree = ast.parse(content) + except SyntaxError as e: + print(f"Syntax error in {filepath}: {e}") + return [] + + # Build a map of class_name -> (class_line, has_docstring, docstring_end_line) + class_info = {} + for node in ast.walk(tree): + if isinstance(node, ast.ClassDef): + has_docstring = False + docstring_end_line = node.lineno # default to class line + + if node.body and isinstance(node.body[0], ast.Expr): + first_stmt = node.body[0] + if isinstance(first_stmt.value, ast.Constant) and isinstance(first_stmt.value.value, str): + has_docstring = True + docstring_end_line = first_stmt.end_lineno or first_stmt.lineno + + class_info[node.name] = (node.lineno, has_docstring, docstring_end_line) + + # Now scan for # auto_docstring comments + classes_to_update = [] + + for i, line in enumerate(lines): + if AUTO_DOCSTRING_PATTERN.match(line): + # Found the marker, look for class definition on next non-empty, non-comment line + j = i + 1 + while j < len(lines): + next_line = lines[j].strip() + if next_line and not next_line.startswith("#"): + break + j += 1 + + if j < len(lines) and lines[j].strip().startswith("class "): + # Extract class name + match = re.match(r"class\s+(\w+)", lines[j].strip()) + if match: + class_name = match.group(1) + if class_name in class_info: + class_line, has_docstring, docstring_end_line = class_info[class_name] + classes_to_update.append((class_name, class_line, has_docstring, docstring_end_line)) + + return classes_to_update + + +def strip_class_name_line(doc: str, class_name: str) -> str: + """Remove the 'class ClassName' line from the doc if present.""" + lines = doc.strip().split("\n") + if lines and lines[0].strip() == f"class {class_name}": + # Remove the class line and any blank line following it + lines = lines[1:] + while lines and not lines[0].strip(): + lines = lines[1:] + return "\n".join(lines) + + +def format_docstring(doc: str, indent: str = " ") -> str: + """Format a doc string as a properly indented docstring.""" + lines = doc.strip().split("\n") + + if len(lines) == 1: + return f'{indent}"""{lines[0]}"""\n' + else: + result = [f'{indent}"""\n'] + for line in lines: + if line.strip(): + result.append(f"{indent}{line}\n") + else: + result.append("\n") + result.append(f'{indent}"""\n') + return "".join(result) + + +def run_ruff_format(filepath: str): + """Run ruff check --fix, ruff format, and doc-builder style on a file to ensure consistent formatting.""" + try: + # First run ruff check --fix to fix any linting issues (including line length) + subprocess.run( + ["ruff", "check", "--fix", filepath], + check=False, # Don't fail if there are unfixable issues + capture_output=True, + text=True, + ) + # Then run ruff format for code formatting + subprocess.run( + ["ruff", "format", filepath], + check=True, + capture_output=True, + text=True, + ) + # Finally run doc-builder style for docstring formatting + subprocess.run( + ["doc-builder", "style", filepath, "--max_len", "119"], + check=False, # Don't fail if doc-builder has issues + capture_output=True, + text=True, + ) + print(f"Formatted {filepath}") + except subprocess.CalledProcessError as e: + print(f"Warning: formatting failed for {filepath}: {e.stderr}") + except FileNotFoundError as e: + print(f"Warning: tool not found ({e}). Skipping formatting.") + except Exception as e: + print(f"Warning: unexpected error formatting {filepath}: {e}") + + +def get_existing_docstring(lines: list, class_line: int, docstring_end_line: int) -> str: + """Extract the existing docstring content from lines.""" + # class_line is 1-indexed, docstring starts at class_line (0-indexed: class_line) + # and ends at docstring_end_line (1-indexed, inclusive) + docstring_lines = lines[class_line:docstring_end_line] + return "".join(docstring_lines) + + +def process_file(filepath: str, overwrite: bool = False) -> list: + """ + Process a file and find/insert docstrings for # auto_docstring marked classes. + + Returns list of classes that need updating. + """ + classes_to_update = find_auto_docstring_classes(filepath) + + if not classes_to_update: + return [] + + if not overwrite: + # Check mode: only verify that docstrings exist + # Content comparison is not reliable due to formatting differences + classes_needing_update = [] + for class_name, class_line, has_docstring, docstring_end_line in classes_to_update: + if not has_docstring: + # No docstring exists, needs update + classes_needing_update.append((filepath, class_name, class_line)) + return classes_needing_update + + # Load the module to get doc properties + module = load_module(filepath) + + with open(filepath, "r", encoding="utf-8", newline="\n") as f: + lines = f.readlines() + + # Process in reverse order to maintain line numbers + updated = False + for class_name, class_line, has_docstring, docstring_end_line in reversed(classes_to_update): + doc = get_doc_from_class(module, class_name) + + if doc is None: + print(f"Warning: Could not get doc for {class_name} in {filepath}") + continue + + # Remove the "class ClassName" line since it's redundant in a docstring + doc = strip_class_name_line(doc, class_name) + + # Format the new docstring with 4-space indent + new_docstring = format_docstring(doc, " ") + + if has_docstring: + # Replace existing docstring (line after class definition to docstring_end_line) + # class_line is 1-indexed, we want to replace from class_line+1 to docstring_end_line + lines = lines[:class_line] + [new_docstring] + lines[docstring_end_line:] + else: + # Insert new docstring right after class definition line + # class_line is 1-indexed, so lines[class_line-1] is the class line + # Insert at position class_line (which is right after the class line) + lines = lines[:class_line] + [new_docstring] + lines[class_line:] + + updated = True + print(f"Updated docstring for {class_name} in {filepath}") + + if updated: + with open(filepath, "w", encoding="utf-8", newline="\n") as f: + f.writelines(lines) + # Run ruff format to ensure consistent line wrapping + run_ruff_format(filepath) + + return [(filepath, cls_name, line) for cls_name, line, _, _ in classes_to_update] + + +def check_auto_docstrings(path: str = None, overwrite: bool = False): + """ + Check all files for # auto_docstring markers and optionally fix them. + """ + if path is None: + path = DIFFUSERS_PATH + + if os.path.isfile(path): + all_files = [path] + else: + all_files = glob.glob(os.path.join(path, "**/*.py"), recursive=True) + + all_markers = [] + + for filepath in all_files: + markers = process_file(filepath, overwrite) + all_markers.extend(markers) + + if not overwrite and len(all_markers) > 0: + message = "\n".join([f"- {f}: {cls} at line {line}" for f, cls, line in all_markers]) + raise ValueError( + f"Found the following # auto_docstring markers that need docstrings:\n{message}\n\n" + f"Run `python utils/modular_auto_docstring.py --fix_and_overwrite` to fix them." + ) + + if overwrite and len(all_markers) > 0: + print(f"\nProcessed {len(all_markers)} docstring(s).") + elif not overwrite and len(all_markers) == 0: + print("All # auto_docstring markers have valid docstrings.") + elif len(all_markers) == 0: + print("No # auto_docstring markers found.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Check and fix # auto_docstring markers in modular pipeline blocks", + ) + parser.add_argument("path", nargs="?", default=None, help="File or directory to process (default: src/diffusers)") + parser.add_argument( + "--fix_and_overwrite", + action="store_true", + help="Whether to fix the docstrings by inserting them from doc property.", + ) + + args = parser.parse_args() + + check_auto_docstrings(args.path, args.fix_and_overwrite) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_benchmarking_status.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_benchmarking_status.py new file mode 100644 index 0000000000000000000000000000000000000000..8a426a15b5edfaa0aeb252d19def51a63c9f189c --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_benchmarking_status.py @@ -0,0 +1,56 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os + +import requests + + +# Configuration +GITHUB_REPO = "huggingface/diffusers" +GITHUB_RUN_ID = os.getenv("GITHUB_RUN_ID") +SLACK_WEBHOOK_URL = os.getenv("SLACK_WEBHOOK_URL") + + +def main(args): + action_url = f"https://github.com/{GITHUB_REPO}/actions/runs/{GITHUB_RUN_ID}" + if args.status == "success": + hub_path = "https://huggingface.co/datasets/diffusers/benchmarks/blob/main/collated_results.csv" + message = ( + "✅ New benchmark workflow successfully run.\n" + f"🕸️ GitHub Action URL: {action_url}.\n" + f"🤗 Check out the benchmarks here: {hub_path}." + ) + else: + message = ( + "❌ Something wrong happened in the benchmarking workflow.\n" + f"Check out the GitHub Action to know more: {action_url}." + ) + + payload = {"text": message} + response = requests.post(SLACK_WEBHOOK_URL, json=payload) + + if response.status_code == 200: + print("Notification sent to Slack successfully.") + else: + print("Failed to send notification to Slack.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--status", type=str, default="success", choices=["success", "failure"]) + args = parser.parse_args() + main(args) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_community_pipelines_mirror.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_community_pipelines_mirror.py new file mode 100644 index 0000000000000000000000000000000000000000..2981f008501f1d437e2df165263c76ddb3939a86 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_community_pipelines_mirror.py @@ -0,0 +1,54 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os + +import requests + + +# Configuration +GITHUB_REPO = "huggingface/diffusers" +GITHUB_RUN_ID = os.getenv("GITHUB_RUN_ID") +SLACK_WEBHOOK_URL = os.getenv("SLACK_WEBHOOK_URL") +PATH_IN_REPO = os.getenv("PATH_IN_REPO") + + +def main(args): + action_url = f"https://github.com/{GITHUB_REPO}/actions/runs/{GITHUB_RUN_ID}" + if args.status == "success": + hub_path = f"https://huggingface.co/datasets/diffusers/community-pipelines-mirror/tree/main/{PATH_IN_REPO}" + message = ( + "✅ Community pipelines successfully mirrored.\n" + f"🕸️ GitHub Action URL: {action_url}.\n" + f"🤗 Hub location: {hub_path}." + ) + else: + message = f"❌ Something wrong happened. Check out the GitHub Action to know more: {action_url}." + + payload = {"text": message} + response = requests.post(SLACK_WEBHOOK_URL, json=payload) + + if response.status_code == 200: + print("Notification sent to Slack successfully.") + else: + print("Failed to send notification to Slack.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--status", type=str, default="success", choices=["success", "failure"]) + args = parser.parse_args() + main(args) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_slack_about_release.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_slack_about_release.py new file mode 100644 index 0000000000000000000000000000000000000000..a68182f8174cfe286d27b7c5c64c4c1258290703 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/notify_slack_about_release.py @@ -0,0 +1,81 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import requests + + +# Configuration +LIBRARY_NAME = "diffusers" +GITHUB_REPO = "huggingface/diffusers" +SLACK_WEBHOOK_URL = os.getenv("SLACK_WEBHOOK_URL") + + +def check_pypi_for_latest_release(library_name): + """Check PyPI for the latest release of the library.""" + response = requests.get(f"https://pypi.org/pypi/{library_name}/json", timeout=60) + if response.status_code == 200: + data = response.json() + return data["info"]["version"] + else: + print("Failed to fetch library details from PyPI.") + return None + + +def get_github_release_info(github_repo): + """Fetch the latest release info from GitHub.""" + url = f"https://api.github.com/repos/{github_repo}/releases/latest" + response = requests.get(url, timeout=60) + + if response.status_code == 200: + data = response.json() + return {"tag_name": data["tag_name"], "url": data["html_url"], "release_time": data["published_at"]} + + else: + print("Failed to fetch release info from GitHub.") + return None + + +def notify_slack(webhook_url, library_name, version, release_info): + """Send a notification to a Slack channel.""" + message = ( + f"🚀 New release for {library_name} available: version **{version}** 🎉\n" + f"📜 Release Notes: {release_info['url']}\n" + f"⏱️ Release time: {release_info['release_time']}" + ) + payload = {"text": message} + response = requests.post(webhook_url, json=payload) + + if response.status_code == 200: + print("Notification sent to Slack successfully.") + else: + print("Failed to send notification to Slack.") + + +def main(): + latest_version = check_pypi_for_latest_release(LIBRARY_NAME) + release_info = get_github_release_info(GITHUB_REPO) + parsed_version = release_info["tag_name"].replace("v", "") + + if latest_version and release_info and latest_version == parsed_version: + notify_slack(SLACK_WEBHOOK_URL, LIBRARY_NAME, latest_version, release_info) + else: + print(f"{latest_version=}, {release_info=}, {parsed_version=}") + raise ValueError("There were some problems.") + + +if __name__ == "__main__": + main() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/overwrite_expected_slice.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/overwrite_expected_slice.py new file mode 100644 index 0000000000000000000000000000000000000000..723c1c98fc21321d9fa86b0d7c2024576eaff3cc --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/overwrite_expected_slice.py @@ -0,0 +1,90 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +from collections import defaultdict + + +def overwrite_file(file, class_name, test_name, correct_line, done_test): + _id = f"{file}_{class_name}_{test_name}" + done_test[_id] += 1 + + with open(file, "r") as f: + lines = f.readlines() + + class_regex = f"class {class_name}(" + test_regex = f"{4 * ' '}def {test_name}(" + line_begin_regex = f"{8 * ' '}{correct_line.split()[0]}" + another_line_begin_regex = f"{16 * ' '}{correct_line.split()[0]}" + in_class = False + in_func = False + in_line = False + insert_line = False + count = 0 + spaces = 0 + + new_lines = [] + for line in lines: + if line.startswith(class_regex): + in_class = True + elif in_class and line.startswith(test_regex): + in_func = True + elif in_class and in_func and (line.startswith(line_begin_regex) or line.startswith(another_line_begin_regex)): + spaces = len(line.split(correct_line.split()[0])[0]) + count += 1 + + if count == done_test[_id]: + in_line = True + + if in_class and in_func and in_line: + if ")" not in line: + continue + else: + insert_line = True + + if in_class and in_func and in_line and insert_line: + new_lines.append(f"{spaces * ' '}{correct_line}") + in_class = in_func = in_line = insert_line = False + else: + new_lines.append(line) + + with open(file, "w") as f: + for line in new_lines: + f.write(line) + + +def main(correct, fail=None): + if fail is not None: + with open(fail, "r") as f: + test_failures = {l.strip() for l in f.readlines()} + else: + test_failures = None + + with open(correct, "r") as f: + correct_lines = f.readlines() + + done_tests = defaultdict(int) + for line in correct_lines: + file, class_name, test_name, correct_line = line.split("::") + if test_failures is None or "::".join([file, class_name, test_name]) in test_failures: + overwrite_file(file, class_name, test_name, correct_line, done_tests) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--correct_filename", help="filename of tests with expected result") + parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None) + args = parser.parse_args() + + main(args.correct_filename, args.fail_filename) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/print_env.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/print_env.py new file mode 100644 index 0000000000000000000000000000000000000000..2fe0777daf7dd266f59ef2c1aaa321d502dd13fe --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/print_env.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python3 + +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# this script dumps information about the environment + +import os +import platform +import sys + + +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" + +print("Python version:", sys.version) + +print("OS platform:", platform.platform()) +print("OS architecture:", platform.machine()) +try: + import psutil + + vm = psutil.virtual_memory() + total_gb = vm.total / (1024**3) + available_gb = vm.available / (1024**3) + print(f"Total RAM: {total_gb:.2f} GB") + print(f"Available RAM: {available_gb:.2f} GB") +except ImportError: + pass + +try: + import torch + + print("Torch version:", torch.__version__) + print("Cuda available:", torch.cuda.is_available()) + if torch.cuda.is_available(): + print("Cuda version:", torch.version.cuda) + print("CuDNN version:", torch.backends.cudnn.version()) + print("Number of GPUs available:", torch.cuda.device_count()) + device_properties = torch.cuda.get_device_properties(0) + total_memory = device_properties.total_memory / (1024**3) + print(f"CUDA memory: {total_memory} GB") + + print("XPU available:", hasattr(torch, "xpu") and torch.xpu.is_available()) + if hasattr(torch, "xpu") and torch.xpu.is_available(): + print("XPU model:", torch.xpu.get_device_properties(0).name) + print("XPU compiler version:", torch.version.xpu) + print("Number of XPUs available:", torch.xpu.device_count()) + device_properties = torch.xpu.get_device_properties(0) + total_memory = device_properties.total_memory / (1024**3) + print(f"XPU memory: {total_memory} GB") + + +except ImportError: + print("Torch version:", None) + +try: + import transformers + + print("transformers version:", transformers.__version__) +except ImportError: + print("transformers version:", None) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/release.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/release.py new file mode 100644 index 0000000000000000000000000000000000000000..a0800b99fbebd9d2956b64fd92d93490e36f9a41 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/release.py @@ -0,0 +1,162 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import re + +import packaging.version + + +PATH_TO_EXAMPLES = "examples/" +REPLACE_PATTERNS = { + "examples": (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'), + "init": (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'), + "setup": (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'), + "doc": (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'), +} +REPLACE_FILES = { + "init": "src/diffusers/__init__.py", + "setup": "setup.py", +} +README_FILE = "README.md" + + +def update_version_in_file(fname, version, pattern): + """Update the version in one file using a specific pattern.""" + with open(fname, "r", encoding="utf-8", newline="\n") as f: + code = f.read() + re_pattern, replace = REPLACE_PATTERNS[pattern] + replace = replace.replace("VERSION", version) + code = re_pattern.sub(replace, code) + with open(fname, "w", encoding="utf-8", newline="\n") as f: + f.write(code) + + +def update_version_in_examples(version): + """Update the version in all examples files.""" + for folder, directories, fnames in os.walk(PATH_TO_EXAMPLES): + # Removing some of the folders with non-actively maintained examples from the walk + if "research_projects" in directories: + directories.remove("research_projects") + if "legacy" in directories: + directories.remove("legacy") + for fname in fnames: + if fname.endswith(".py"): + update_version_in_file(os.path.join(folder, fname), version, pattern="examples") + + +def global_version_update(version, patch=False): + """Update the version in all needed files.""" + for pattern, fname in REPLACE_FILES.items(): + update_version_in_file(fname, version, pattern) + if not patch: + update_version_in_examples(version) + + +def clean_main_ref_in_model_list(): + """Replace the links from main doc tp stable doc in the model list of the README.""" + # If the introduction or the conclusion of the list change, the prompts may need to be updated. + _start_prompt = "🤗 Transformers currently provides the following architectures" + _end_prompt = "1. Want to contribute a new model?" + with open(README_FILE, "r", encoding="utf-8", newline="\n") as f: + lines = f.readlines() + + # Find the start of the list. + start_index = 0 + while not lines[start_index].startswith(_start_prompt): + start_index += 1 + start_index += 1 + + index = start_index + # Update the lines in the model list. + while not lines[index].startswith(_end_prompt): + if lines[index].startswith("1."): + lines[index] = lines[index].replace( + "https://huggingface.co/docs/diffusers/main/model_doc", + "https://huggingface.co/docs/diffusers/model_doc", + ) + index += 1 + + with open(README_FILE, "w", encoding="utf-8", newline="\n") as f: + f.writelines(lines) + + +def get_version(): + """Reads the current version in the __init__.""" + with open(REPLACE_FILES["init"], "r") as f: + code = f.read() + default_version = REPLACE_PATTERNS["init"][0].search(code).groups()[0] + return packaging.version.parse(default_version) + + +def pre_release_work(patch=False): + """Do all the necessary pre-release steps.""" + # First let's get the default version: base version if we are in dev, bump minor otherwise. + default_version = get_version() + if patch and default_version.is_devrelease: + raise ValueError("Can't create a patch version from the dev branch, checkout a released version!") + if default_version.is_devrelease: + default_version = default_version.base_version + elif patch: + default_version = f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}" + else: + default_version = f"{default_version.major}.{default_version.minor + 1}.0" + + # Now let's ask nicely if that's the right one. + version = input(f"Which version are you releasing? [{default_version}]") + if len(version) == 0: + version = default_version + + print(f"Updating version to {version}.") + global_version_update(version, patch=patch) + + +# if not patch: +# print("Cleaning main README, don't forget to run `make fix-copies`.") +# clean_main_ref_in_model_list() + + +def post_release_work(): + """Do all the necessary post-release steps.""" + # First let's get the current version + current_version = get_version() + dev_version = f"{current_version.major}.{current_version.minor + 1}.0.dev0" + current_version = current_version.base_version + + # Check with the user we got that right. + version = input(f"Which version are we developing now? [{dev_version}]") + if len(version) == 0: + version = dev_version + + print(f"Updating version to {version}.") + global_version_update(version) + + +# print("Cleaning main README, don't forget to run `make fix-copies`.") +# clean_main_ref_in_model_list() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.") + parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.") + args = parser.parse_args() + if not args.post_release: + pre_release_work(patch=args.patch) + elif args.patch: + print("Nothing to do after a patch :-)") + else: + post_release_work() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/stale.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/stale.py new file mode 100644 index 0000000000000000000000000000000000000000..b92fb83ceb4cd531f86edc590f4e22c674787503 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/stale.py @@ -0,0 +1,69 @@ +# Copyright 2025 The HuggingFace Team, the AllenNLP library authors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Script to close stale issue. Taken in part from the AllenNLP repository. +https://github.com/allenai/allennlp. +""" + +import os +from datetime import datetime as dt +from datetime import timezone + +from github import Github + + +LABELS_TO_EXEMPT = [ + "close-to-merge", + "good first issue", + "good second issue", + "good difficult issue", + "enhancement", + "new pipeline/model", + "new scheduler", + "wip", +] + + +def main(): + g = Github(os.environ["GITHUB_TOKEN"]) + repo = g.get_repo("huggingface/diffusers") + open_issues = repo.get_issues(state="open") + + for issue in open_issues: + labels = [label.name.lower() for label in issue.get_labels()] + if "stale" in labels: + comments = sorted(issue.get_comments(), key=lambda i: i.created_at, reverse=True) + last_comment = comments[0] if len(comments) > 0 else None + if last_comment is not None and last_comment.user.login != "github-actions[bot]": + # Opens the issue if someone other than Stalebot commented. + issue.edit(state="open") + issue.remove_from_labels("stale") + elif ( + (dt.now(timezone.utc) - issue.updated_at).days > 23 + and (dt.now(timezone.utc) - issue.created_at).days >= 30 + and not any(label in LABELS_TO_EXEMPT for label in labels) + ): + # Post a Stalebot notification after 23 days of inactivity. + issue.create_comment( + "This issue has been automatically marked as stale because it has not had " + "recent activity. If you think this still needs to be addressed " + "please comment on this thread.\n\nPlease note that issues that do not follow the " + "[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) " + "are likely to be ignored." + ) + issue.add_to_labels("stale") + + +if __name__ == "__main__": + main() diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/tests_fetcher.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/tests_fetcher.py new file mode 100644 index 0000000000000000000000000000000000000000..59ef5499cc2a12fd12c251c0604f883b2aaca893 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/tests_fetcher.py @@ -0,0 +1,1128 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Welcome to tests_fetcher V2. + +This util is designed to fetch tests to run on a PR so that only the tests impacted by the modifications are run, and +when too many models are being impacted, only run the tests of a subset of core models. It works like this. + +Stage 1: Identify the modified files. For jobs that run on the main branch, it's just the diff with the last commit. +On a PR, this takes all the files from the branching point to the current commit (so all modifications in a PR, not +just the last commit) but excludes modifications that are on docstrings or comments only. + +Stage 2: Extract the tests to run. This is done by looking at the imports in each module and test file: if module A +imports module B, then changing module B impacts module A, so the tests using module A should be run. We thus get the +dependencies of each model and then recursively builds the 'reverse' map of dependencies to get all modules and tests +impacted by a given file. We then only keep the tests (and only the core models tests if there are too many modules). + +Caveats: + - This module only filters tests by files (not individual tests) so it's better to have tests for different things + in different files. + - This module assumes inits are just importing things, not really building objects, so it's better to structure + them this way and move objects building in separate submodules. + +Usage: + +Base use to fetch the tests in a pull request + +```bash +python utils/tests_fetcher.py +``` + +Base use to fetch the tests on a the main branch (with diff from the last commit): + +```bash +python utils/tests_fetcher.py --diff_with_last_commit +``` +""" + +import argparse +import collections +import json +import os +import re +from contextlib import contextmanager +from pathlib import Path +from typing import Dict, List, Tuple, Union + +from git import Repo + + +PATH_TO_REPO = Path(__file__).parent.parent.resolve() +PATH_TO_EXAMPLES = PATH_TO_REPO / "examples" +PATH_TO_DIFFUSERS = PATH_TO_REPO / "src/diffusers" +PATH_TO_TESTS = PATH_TO_REPO / "tests" + +# Ignore fixtures in tests folder +# Ignore lora since they are always tested +MODULES_TO_IGNORE = ["fixtures", "lora"] + +IMPORTANT_PIPELINES = [ + "controlnet", + "stable_diffusion", + "stable_diffusion_2", + "stable_diffusion_xl", + "stable_video_diffusion", + "deepfloyd_if", + "kandinsky", + "kandinsky2_2", + "text_to_video_synthesis", + "wuerstchen", +] + + +@contextmanager +def checkout_commit(repo: Repo, commit_id: str): + """ + Context manager that checks out a given commit when entered, but gets back to the reference it was at on exit. + + Args: + repo (`git.Repo`): A git repository (for instance the Transformers repo). + commit_id (`str`): The commit reference to checkout inside the context manager. + """ + current_head = repo.head.commit if repo.head.is_detached else repo.head.ref + + try: + repo.git.checkout(commit_id) + yield + + finally: + repo.git.checkout(current_head) + + +def clean_code(content: str) -> str: + """ + Remove docstrings, empty line or comments from some code (used to detect if a diff is real or only concern + comments or docstrings). + + Args: + content (`str`): The code to clean + + Returns: + `str`: The cleaned code. + """ + # We need to deactivate autoformatting here to write escaped triple quotes (we cannot use real triple quotes or + # this would mess up the result if this function applied to this particular file). + # fmt: off + # Remove docstrings by splitting on triple " then triple ': + splits = content.split('\"\"\"') + content = "".join(splits[::2]) + splits = content.split("\'\'\'") + # fmt: on + content = "".join(splits[::2]) + + # Remove empty lines and comments + lines_to_keep = [] + for line in content.split("\n"): + # remove anything that is after a # sign. + line = re.sub("#.*$", "", line) + # remove white lines + if len(line) != 0 and not line.isspace(): + lines_to_keep.append(line) + return "\n".join(lines_to_keep) + + +def keep_doc_examples_only(content: str) -> str: + """ + Remove everything from the code content except the doc examples (used to determined if a diff should trigger doc + tests or not). + + Args: + content (`str`): The code to clean + + Returns: + `str`: The cleaned code. + """ + # Keep doc examples only by splitting on triple "`" + splits = content.split("```") + # Add leading and trailing "```" so the navigation is easier when compared to the original input `content` + content = "```" + "```".join(splits[1::2]) + "```" + + # Remove empty lines and comments + lines_to_keep = [] + for line in content.split("\n"): + # remove anything that is after a # sign. + line = re.sub("#.*$", "", line) + # remove white lines + if len(line) != 0 and not line.isspace(): + lines_to_keep.append(line) + return "\n".join(lines_to_keep) + + +def get_all_tests() -> List[str]: + """ + Walks the `tests` folder to return a list of files/subfolders. This is used to split the tests to run when using + parallelism. The split is: + + - folders under `tests`: (`tokenization`, `pipelines`, etc) except the subfolder `models` is excluded. + - folders under `tests/models`: `bert`, `gpt2`, etc. + - test files under `tests`: `test_modeling_common.py`, `test_tokenization_common.py`, etc. + """ + + # test folders/files directly under `tests` folder + tests = os.listdir(PATH_TO_TESTS) + tests = [f"tests/{f}" for f in tests if "__pycache__" not in f] + tests = sorted([f for f in tests if (PATH_TO_REPO / f).is_dir() or f.startswith("tests/test_")]) + + return tests + + +def diff_is_docstring_only(repo: Repo, branching_point: str, filename: str) -> bool: + """ + Check if the diff is only in docstrings (or comments and whitespace) in a filename. + + Args: + repo (`git.Repo`): A git repository (for instance the Transformers repo). + branching_point (`str`): The commit reference of where to compare for the diff. + filename (`str`): The filename where we want to know if the diff isonly in docstrings/comments. + + Returns: + `bool`: Whether the diff is docstring/comments only or not. + """ + folder = Path(repo.working_dir) + with checkout_commit(repo, branching_point): + with open(folder / filename, "r", encoding="utf-8") as f: + old_content = f.read() + + with open(folder / filename, "r", encoding="utf-8") as f: + new_content = f.read() + + old_content_clean = clean_code(old_content) + new_content_clean = clean_code(new_content) + + return old_content_clean == new_content_clean + + +def diff_contains_doc_examples(repo: Repo, branching_point: str, filename: str) -> bool: + """ + Check if the diff is only in code examples of the doc in a filename. + + Args: + repo (`git.Repo`): A git repository (for instance the Transformers repo). + branching_point (`str`): The commit reference of where to compare for the diff. + filename (`str`): The filename where we want to know if the diff is only in codes examples. + + Returns: + `bool`: Whether the diff is only in code examples of the doc or not. + """ + folder = Path(repo.working_dir) + with checkout_commit(repo, branching_point): + with open(folder / filename, "r", encoding="utf-8") as f: + old_content = f.read() + + with open(folder / filename, "r", encoding="utf-8") as f: + new_content = f.read() + + old_content_clean = keep_doc_examples_only(old_content) + new_content_clean = keep_doc_examples_only(new_content) + + return old_content_clean != new_content_clean + + +def get_diff(repo: Repo, base_commit: str, commits: List[str]) -> List[str]: + """ + Get the diff between a base commit and one or several commits. + + Args: + repo (`git.Repo`): + A git repository (for instance the Transformers repo). + base_commit (`str`): + The commit reference of where to compare for the diff. This is the current commit, not the branching point! + commits (`List[str]`): + The list of commits with which to compare the repo at `base_commit` (so the branching point). + + Returns: + `List[str]`: The list of Python files with a diff (files added, renamed or deleted are always returned, files + modified are returned if the diff in the file is not only in docstrings or comments, see + `diff_is_docstring_only`). + """ + print("\n### DIFF ###\n") + code_diff = [] + for commit in commits: + for diff_obj in commit.diff(base_commit): + # We always add new python files + if diff_obj.change_type == "A" and diff_obj.b_path.endswith(".py"): + code_diff.append(diff_obj.b_path) + # We check that deleted python files won't break corresponding tests. + elif diff_obj.change_type == "D" and diff_obj.a_path.endswith(".py"): + code_diff.append(diff_obj.a_path) + # Now for modified files + elif diff_obj.change_type in ["M", "R"] and diff_obj.b_path.endswith(".py"): + # In case of renames, we'll look at the tests using both the old and new name. + if diff_obj.a_path != diff_obj.b_path: + code_diff.extend([diff_obj.a_path, diff_obj.b_path]) + else: + # Otherwise, we check modifications are in code and not docstrings. + if diff_is_docstring_only(repo, commit, diff_obj.b_path): + print(f"Ignoring diff in {diff_obj.b_path} as it only concerns docstrings or comments.") + else: + code_diff.append(diff_obj.a_path) + + return code_diff + + +def get_modified_python_files(diff_with_last_commit: bool = False) -> List[str]: + """ + Return a list of python files that have been modified between: + + - the current head and the main branch if `diff_with_last_commit=False` (default) + - the current head and its parent commit otherwise. + + Returns: + `List[str]`: The list of Python files with a diff (files added, renamed or deleted are always returned, files + modified are returned if the diff in the file is not only in docstrings or comments, see + `diff_is_docstring_only`). + """ + repo = Repo(PATH_TO_REPO) + + if not diff_with_last_commit: + # Need to fetch refs for main using remotes when running with github actions. + upstream_main = repo.remotes.origin.refs.main + + print(f"main is at {upstream_main.commit}") + print(f"Current head is at {repo.head.commit}") + + branching_commits = repo.merge_base(upstream_main, repo.head) + for commit in branching_commits: + print(f"Branching commit: {commit}") + return get_diff(repo, repo.head.commit, branching_commits) + else: + print(f"main is at {repo.head.commit}") + parent_commits = repo.head.commit.parents + for commit in parent_commits: + print(f"Parent commit: {commit}") + return get_diff(repo, repo.head.commit, parent_commits) + + +def get_diff_for_doctesting(repo: Repo, base_commit: str, commits: List[str]) -> List[str]: + """ + Get the diff in doc examples between a base commit and one or several commits. + + Args: + repo (`git.Repo`): + A git repository (for instance the Transformers repo). + base_commit (`str`): + The commit reference of where to compare for the diff. This is the current commit, not the branching point! + commits (`List[str]`): + The list of commits with which to compare the repo at `base_commit` (so the branching point). + + Returns: + `List[str]`: The list of Python and Markdown files with a diff (files added or renamed are always returned, files + modified are returned if the diff in the file is only in doctest examples). + """ + print("\n### DIFF ###\n") + code_diff = [] + for commit in commits: + for diff_obj in commit.diff(base_commit): + # We only consider Python files and doc files. + if not diff_obj.b_path.endswith(".py") and not diff_obj.b_path.endswith(".md"): + continue + # We always add new python/md files + if diff_obj.change_type in ["A"]: + code_diff.append(diff_obj.b_path) + # Now for modified files + elif diff_obj.change_type in ["M", "R"]: + # In case of renames, we'll look at the tests using both the old and new name. + if diff_obj.a_path != diff_obj.b_path: + code_diff.extend([diff_obj.a_path, diff_obj.b_path]) + else: + # Otherwise, we check modifications contain some doc example(s). + if diff_contains_doc_examples(repo, commit, diff_obj.b_path): + code_diff.append(diff_obj.a_path) + else: + print(f"Ignoring diff in {diff_obj.b_path} as it doesn't contain any doc example.") + + return code_diff + + +def get_all_doctest_files() -> List[str]: + """ + Return the complete list of python and Markdown files on which we run doctest. + + At this moment, we restrict this to only take files from `src/` or `docs/source/en/` that are not in `utils/not_doctested.txt`. + + Returns: + `List[str]`: The complete list of Python and Markdown files on which we run doctest. + """ + py_files = [str(x.relative_to(PATH_TO_REPO)) for x in PATH_TO_REPO.glob("**/*.py")] + md_files = [str(x.relative_to(PATH_TO_REPO)) for x in PATH_TO_REPO.glob("**/*.md")] + test_files_to_run = py_files + md_files + + # only include files in `src` or `docs/source/en/` + test_files_to_run = [x for x in test_files_to_run if x.startswith(("src/", "docs/source/en/"))] + # not include init files + test_files_to_run = [x for x in test_files_to_run if not x.endswith(("__init__.py",))] + + # These are files not doctested yet. + with open("utils/not_doctested.txt") as fp: + not_doctested = {x.split(" ")[0] for x in fp.read().strip().split("\n")} + + # So far we don't have 100% coverage for doctest. This line will be removed once we achieve 100%. + test_files_to_run = [x for x in test_files_to_run if x not in not_doctested] + + return sorted(test_files_to_run) + + +def get_new_doctest_files(repo, base_commit, branching_commit) -> List[str]: + """ + Get the list of files that were removed from "utils/not_doctested.txt", between `base_commit` and + `branching_commit`. + + Returns: + `List[str]`: List of files that were removed from "utils/not_doctested.txt". + """ + for diff_obj in branching_commit.diff(base_commit): + # Ignores all but the "utils/not_doctested.txt" file. + if diff_obj.a_path != "utils/not_doctested.txt": + continue + # Loads the two versions + folder = Path(repo.working_dir) + with checkout_commit(repo, branching_commit): + with open(folder / "utils/not_doctested.txt", "r", encoding="utf-8") as f: + old_content = f.read() + with open(folder / "utils/not_doctested.txt", "r", encoding="utf-8") as f: + new_content = f.read() + # Compute the removed lines and return them + removed_content = {x.split(" ")[0] for x in old_content.split("\n")} - { + x.split(" ")[0] for x in new_content.split("\n") + } + return sorted(removed_content) + return [] + + +def get_doctest_files(diff_with_last_commit: bool = False) -> List[str]: + """ + Return a list of python and Markdown files where doc example have been modified between: + + - the current head and the main branch if `diff_with_last_commit=False` (default) + - the current head and its parent commit otherwise. + + Returns: + `List[str]`: The list of Python and Markdown files with a diff (files added or renamed are always returned, files + modified are returned if the diff in the file is only in doctest examples). + """ + repo = Repo(PATH_TO_REPO) + + test_files_to_run = [] # noqa + if not diff_with_last_commit: + upstream_main = repo.remotes.origin.refs.main + print(f"main is at {upstream_main.commit}") + print(f"Current head is at {repo.head.commit}") + + branching_commits = repo.merge_base(upstream_main, repo.head) + for commit in branching_commits: + print(f"Branching commit: {commit}") + test_files_to_run = get_diff_for_doctesting(repo, repo.head.commit, branching_commits) + else: + print(f"main is at {repo.head.commit}") + parent_commits = repo.head.commit.parents + for commit in parent_commits: + print(f"Parent commit: {commit}") + test_files_to_run = get_diff_for_doctesting(repo, repo.head.commit, parent_commits) + + all_test_files_to_run = get_all_doctest_files() + + # Add to the test files to run any removed entry from "utils/not_doctested.txt". + new_test_files = get_new_doctest_files(repo, repo.head.commit, upstream_main.commit) + test_files_to_run = list(set(test_files_to_run + new_test_files)) + + # Do not run slow doctest tests on CircleCI + with open("utils/slow_documentation_tests.txt") as fp: + slow_documentation_tests = set(fp.read().strip().split("\n")) + test_files_to_run = [ + x for x in test_files_to_run if x in all_test_files_to_run and x not in slow_documentation_tests + ] + + # Make sure we did not end up with a test file that was removed + test_files_to_run = [f for f in test_files_to_run if (PATH_TO_REPO / f).exists()] + + return sorted(test_files_to_run) + + +# (:?^|\n) -> Non-catching group for the beginning of the doc or a new line. +# \s*from\s+(\.+\S+)\s+import\s+([^\n]+) -> Line only contains from .xxx import yyy and we catch .xxx and yyy +# (?=\n) -> Look-ahead to a new line. We can't just put \n here or using find_all on this re will only catch every +# other import. +_re_single_line_relative_imports = re.compile(r"(?:^|\n)\s*from\s+(\.+\S+)\s+import\s+([^\n]+)(?=\n)") +# (:?^|\n) -> Non-catching group for the beginning of the doc or a new line. +# \s*from\s+(\.+\S+)\s+import\s+\(([^\)]+)\) -> Line continues with from .xxx import (yyy) and we catch .xxx and yyy +# yyy will take multiple lines otherwise there wouldn't be parenthesis. +_re_multi_line_relative_imports = re.compile(r"(?:^|\n)\s*from\s+(\.+\S+)\s+import\s+\(([^\)]+)\)") +# (:?^|\n) -> Non-catching group for the beginning of the doc or a new line. +# \s*from\s+transformers(\S*)\s+import\s+([^\n]+) -> Line only contains from transformers.xxx import yyy and we catch +# .xxx and yyy +# (?=\n) -> Look-ahead to a new line. We can't just put \n here or using find_all on this re will only catch every +# other import. +_re_single_line_direct_imports = re.compile(r"(?:^|\n)\s*from\s+diffusers(\S*)\s+import\s+([^\n]+)(?=\n)") +# (:?^|\n) -> Non-catching group for the beginning of the doc or a new line. +# \s*from\s+transformers(\S*)\s+import\s+\(([^\)]+)\) -> Line continues with from transformers.xxx import (yyy) and we +# catch .xxx and yyy. yyy will take multiple lines otherwise there wouldn't be parenthesis. +_re_multi_line_direct_imports = re.compile(r"(?:^|\n)\s*from\s+diffusers(\S*)\s+import\s+\(([^\)]+)\)") + + +def extract_imports(module_fname: str, cache: Dict[str, List[str]] = None) -> List[str]: + """ + Get the imports a given module makes. + + Args: + module_fname (`str`): + The name of the file of the module where we want to look at the imports (given relative to the root of + the repo). + cache (Dictionary `str` to `List[str]`, *optional*): + To speed up this function if it was previously called on `module_fname`, the cache of all previously + computed results. + + Returns: + `List[str]`: The list of module filenames imported in the input `module_fname` (a submodule we import from that + is a subfolder will give its init file). + """ + if cache is not None and module_fname in cache: + return cache[module_fname] + + with open(PATH_TO_REPO / module_fname, "r", encoding="utf-8") as f: + content = f.read() + + # Filter out all docstrings to not get imports in code examples. As before we need to deactivate formatting to + # keep this as escaped quotes and avoid this function failing on this file. + # fmt: off + splits = content.split('\"\"\"') + # fmt: on + content = "".join(splits[::2]) + + module_parts = str(module_fname).split(os.path.sep) + imported_modules = [] + + # Let's start with relative imports + relative_imports = _re_single_line_relative_imports.findall(content) + relative_imports = [ + (mod, imp) for mod, imp in relative_imports if "# tests_ignore" not in imp and imp.strip() != "(" + ] + multiline_relative_imports = _re_multi_line_relative_imports.findall(content) + relative_imports += [(mod, imp) for mod, imp in multiline_relative_imports if "# tests_ignore" not in imp] + + # We need to remove parts of the module name depending on the depth of the relative imports. + for module, imports in relative_imports: + level = 0 + while module.startswith("."): + module = module[1:] + level += 1 + + if len(module) > 0: + dep_parts = module_parts[: len(module_parts) - level] + module.split(".") + else: + dep_parts = module_parts[: len(module_parts) - level] + imported_module = os.path.sep.join(dep_parts) + imported_modules.append((imported_module, [imp.strip() for imp in imports.split(",")])) + + # Let's continue with direct imports + direct_imports = _re_single_line_direct_imports.findall(content) + direct_imports = [(mod, imp) for mod, imp in direct_imports if "# tests_ignore" not in imp and imp.strip() != "("] + multiline_direct_imports = _re_multi_line_direct_imports.findall(content) + direct_imports += [(mod, imp) for mod, imp in multiline_direct_imports if "# tests_ignore" not in imp] + + # We need to find the relative path of those imports. + for module, imports in direct_imports: + import_parts = module.split(".")[1:] # ignore the name of the repo since we add it below. + dep_parts = ["src", "diffusers"] + import_parts + imported_module = os.path.sep.join(dep_parts) + imported_modules.append((imported_module, [imp.strip() for imp in imports.split(",")])) + + result = [] + # Double check we get proper modules (either a python file or a folder with an init). + for module_file, imports in imported_modules: + if (PATH_TO_REPO / f"{module_file}.py").is_file(): + module_file = f"{module_file}.py" + elif (PATH_TO_REPO / module_file).is_dir() and (PATH_TO_REPO / module_file / "__init__.py").is_file(): + module_file = os.path.sep.join([module_file, "__init__.py"]) + imports = [imp for imp in imports if len(imp) > 0 and re.match("^[A-Za-z0-9_]*$", imp)] + if len(imports) > 0: + result.append((module_file, imports)) + + if cache is not None: + cache[module_fname] = result + + return result + + +def get_module_dependencies(module_fname: str, cache: Dict[str, List[str]] = None) -> List[str]: + """ + Refines the result of `extract_imports` to remove subfolders and get a proper list of module filenames: if a file + as an import `from utils import Foo, Bar`, with `utils` being a subfolder containing many files, this will traverse + the `utils` init file to check where those dependencies come from: for instance the files utils/foo.py and utils/bar.py. + + Warning: This presupposes that all intermediate inits are properly built (with imports from the respective + submodules) and work better if objects are defined in submodules and not the intermediate init (otherwise the + intermediate init is added, and inits usually have a lot of dependencies). + + Args: + module_fname (`str`): + The name of the file of the module where we want to look at the imports (given relative to the root of + the repo). + cache (Dictionary `str` to `List[str]`, *optional*): + To speed up this function if it was previously called on `module_fname`, the cache of all previously + computed results. + + Returns: + `List[str]`: The list of module filenames imported in the input `module_fname` (with submodule imports refined). + """ + dependencies = [] + imported_modules = extract_imports(module_fname, cache=cache) + # The while loop is to recursively traverse all inits we may encounter: we will add things as we go. + while len(imported_modules) > 0: + new_modules = [] + for module, imports in imported_modules: + # If we end up in an __init__ we are often not actually importing from this init (except in the case where + # the object is fully defined in the __init__) + if module.endswith("__init__.py"): + # So we get the imports from that init then try to find where our objects come from. + new_imported_modules = extract_imports(module, cache=cache) + for new_module, new_imports in new_imported_modules: + if any(i in new_imports for i in imports): + if new_module not in dependencies: + new_modules.append((new_module, [i for i in new_imports if i in imports])) + imports = [i for i in imports if i not in new_imports] + if len(imports) > 0: + # If there are any objects lefts, they may be a submodule + path_to_module = PATH_TO_REPO / module.replace("__init__.py", "") + dependencies.extend( + [ + os.path.join(module.replace("__init__.py", ""), f"{i}.py") + for i in imports + if (path_to_module / f"{i}.py").is_file() + ] + ) + imports = [i for i in imports if not (path_to_module / f"{i}.py").is_file()] + if len(imports) > 0: + # Then if there are still objects left, they are fully defined in the init, so we keep it as a + # dependency. + dependencies.append(module) + else: + dependencies.append(module) + + imported_modules = new_modules + + return dependencies + + +def create_reverse_dependency_tree() -> List[Tuple[str, str]]: + """ + Create a list of all edges (a, b) which mean that modifying a impacts b with a going over all module and test files. + """ + cache = {} + all_modules = list(PATH_TO_DIFFUSERS.glob("**/*.py")) + list(PATH_TO_TESTS.glob("**/*.py")) + all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules] + edges = [(dep, mod) for mod in all_modules for dep in get_module_dependencies(mod, cache=cache)] + + return list(set(edges)) + + +def get_tree_starting_at(module: str, edges: List[Tuple[str, str]]) -> List[Union[str, List[str]]]: + """ + Returns the tree starting at a given module following all edges. + + Args: + module (`str`): The module that will be the root of the subtree we want. + edges (`List[Tuple[str, str]]`): The list of all edges of the tree. + + Returns: + `List[Union[str, List[str]]]`: The tree to print in the following format: [module, [list of edges + starting at module], [list of edges starting at the preceding level], ...] + """ + vertices_seen = [module] + new_edges = [edge for edge in edges if edge[0] == module and edge[1] != module and "__init__.py" not in edge[1]] + tree = [module] + while len(new_edges) > 0: + tree.append(new_edges) + final_vertices = list({edge[1] for edge in new_edges}) + vertices_seen.extend(final_vertices) + new_edges = [ + edge + for edge in edges + if edge[0] in final_vertices and edge[1] not in vertices_seen and "__init__.py" not in edge[1] + ] + + return tree + + +def print_tree_deps_of(module, all_edges=None): + """ + Prints the tree of modules depending on a given module. + + Args: + module (`str`): The module that will be the root of the subtree we want. + all_edges (`List[Tuple[str, str]]`, *optional*): + The list of all edges of the tree. Will be set to `create_reverse_dependency_tree()` if not passed. + """ + if all_edges is None: + all_edges = create_reverse_dependency_tree() + tree = get_tree_starting_at(module, all_edges) + + # The list of lines is a list of tuples (line_to_be_printed, module) + # Keeping the modules lets us know where to insert each new lines in the list. + lines = [(tree[0], tree[0])] + for index in range(1, len(tree)): + edges = tree[index] + start_edges = {edge[0] for edge in edges} + + for start in start_edges: + end_edges = {edge[1] for edge in edges if edge[0] == start} + # We will insert all those edges just after the line showing start. + pos = 0 + while lines[pos][1] != start: + pos += 1 + lines = lines[: pos + 1] + [(" " * (2 * index) + end, end) for end in end_edges] + lines[pos + 1 :] + + for line in lines: + # We don't print the refs that where just here to help build lines. + print(line[0]) + + +def init_test_examples_dependencies() -> Tuple[Dict[str, List[str]], List[str]]: + """ + The test examples do not import from the examples (which are just scripts, not modules) so we need some extra + care initializing the dependency map, which is the goal of this function. It initializes the dependency map for + example files by linking each example to the example test file for the example framework. + + Returns: + `Tuple[Dict[str, List[str]], List[str]]`: A tuple with two elements: the initialized dependency map which is a + dict test example file to list of example files potentially tested by that test file, and the list of all + example files (to avoid recomputing it later). + """ + test_example_deps = {} + all_examples = [] + for framework in ["flax", "pytorch", "tensorflow"]: + test_files = list((PATH_TO_EXAMPLES / framework).glob("test_*.py")) + all_examples.extend(test_files) + # Remove the files at the root of examples/framework since they are not proper examples (they are either utils + # or example test files). + examples = [ + f for f in (PATH_TO_EXAMPLES / framework).glob("**/*.py") if f.parent != PATH_TO_EXAMPLES / framework + ] + all_examples.extend(examples) + for test_file in test_files: + with open(test_file, "r", encoding="utf-8") as f: + content = f.read() + # Map all examples to the test files found in examples/framework. + test_example_deps[str(test_file.relative_to(PATH_TO_REPO))] = [ + str(e.relative_to(PATH_TO_REPO)) for e in examples if e.name in content + ] + # Also map the test files to themselves. + test_example_deps[str(test_file.relative_to(PATH_TO_REPO))].append( + str(test_file.relative_to(PATH_TO_REPO)) + ) + return test_example_deps, all_examples + + +def create_reverse_dependency_map() -> dict[str, List[str]]: + """ + Create the dependency map from module/test filename to the list of modules/tests that depend on it recursively. + + Returns: + `Dict[str, List[str]]`: The reverse dependency map as a dictionary mapping filenames to all the filenames + depending on it recursively. This way the tests impacted by a change in file A are the test files in the list + corresponding to key A in this result. + """ + cache = {} + # Start from the example deps init. + example_deps, examples = init_test_examples_dependencies() + # Add all modules and all tests to all examples + all_modules = list(PATH_TO_DIFFUSERS.glob("**/*.py")) + list(PATH_TO_TESTS.glob("**/*.py")) + examples + all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules] + # Compute the direct dependencies of all modules. + direct_deps = {m: get_module_dependencies(m, cache=cache) for m in all_modules} + direct_deps.update(example_deps) + + # This recurses the dependencies + something_changed = True + while something_changed: + something_changed = False + for m in all_modules: + for d in direct_deps[m]: + # We stop recursing at an init (cause we always end up in the main init and we don't want to add all + # files which the main init imports) + if d.endswith("__init__.py"): + continue + if d not in direct_deps: + raise ValueError(f"KeyError:{d}. From {m}") + new_deps = set(direct_deps[d]) - set(direct_deps[m]) + if len(new_deps) > 0: + direct_deps[m].extend(list(new_deps)) + something_changed = True + + # Finally we can build the reverse map. + reverse_map = collections.defaultdict(list) + for m in all_modules: + for d in direct_deps[m]: + reverse_map[d].append(m) + + # For inits, we don't do the reverse deps but the direct deps: if modifying an init, we want to make sure we test + # all the modules impacted by that init. + for m in [f for f in all_modules if f.endswith("__init__.py")]: + direct_deps = get_module_dependencies(m, cache=cache) + deps = sum([reverse_map[d] for d in direct_deps if not d.endswith("__init__.py")], direct_deps) + reverse_map[m] = list(set(deps) - {m}) + + return reverse_map + + +def create_module_to_test_map(reverse_map: Dict[str, List[str]] = None) -> dict[str, List[str]]: + """ + Extract the tests from the reverse_dependency_map and potentially filters the model tests. + + Args: + reverse_map (`Dict[str, List[str]]`, *optional*): + The reverse dependency map as created by `create_reverse_dependency_map`. Will default to the result of + that function if not provided. + filter_pipelines (`bool`, *optional*, defaults to `False`): + Whether or not to filter pipeline tests to only include core pipelines if a file impacts a lot of models. + + Returns: + `Dict[str, List[str]]`: A dictionary that maps each file to the tests to execute if that file was modified. + """ + if reverse_map is None: + reverse_map = create_reverse_dependency_map() + + # Utility that tells us if a given file is a test (taking test examples into account) + def is_test(fname): + if fname.startswith("tests"): + return True + if fname.startswith("examples") and fname.split(os.path.sep)[-1].startswith("test"): + return True + return False + + # Build the test map + test_map = {module: [f for f in deps if is_test(f)] for module, deps in reverse_map.items()} + + return test_map + + +def check_imports_all_exist(): + """ + Isn't used per se by the test fetcher but might be used later as a quality check. Putting this here for now so the + code is not lost. This checks all imports in a given file do exist. + """ + cache = {} + all_modules = list(PATH_TO_DIFFUSERS.glob("**/*.py")) + list(PATH_TO_TESTS.glob("**/*.py")) + all_modules = [str(mod.relative_to(PATH_TO_REPO)) for mod in all_modules] + direct_deps = {m: get_module_dependencies(m, cache=cache) for m in all_modules} + + for module, deps in direct_deps.items(): + for dep in deps: + if not (PATH_TO_REPO / dep).is_file(): + print(f"{module} has dependency on {dep} which does not exist.") + + +def _print_list(l) -> str: + """ + Pretty print a list of elements with one line per element and a - starting each line. + """ + return "\n".join([f"- {f}" for f in l]) + + +def update_test_map_with_core_pipelines(json_output_file: str): + print(f"\n### ADD CORE PIPELINE TESTS ###\n{_print_list(IMPORTANT_PIPELINES)}") + with open(json_output_file, "rb") as fp: + test_map = json.load(fp) + + # Add core pipelines as their own test group + test_map["core_pipelines"] = " ".join( + sorted([str(PATH_TO_TESTS / f"pipelines/{pipe}") for pipe in IMPORTANT_PIPELINES]) + ) + + # If there are no existing pipeline tests save the map + if "pipelines" not in test_map: + with open(json_output_file, "w", encoding="UTF-8") as fp: + json.dump(test_map, fp, ensure_ascii=False) + + pipeline_tests = test_map.pop("pipelines") + pipeline_tests = pipeline_tests.split(" ") + + # Remove core pipeline tests from the fetched pipeline tests + updated_pipeline_tests = [] + for pipe in pipeline_tests: + if pipe == "tests/pipelines" or Path(pipe).parts[2] in IMPORTANT_PIPELINES: + continue + updated_pipeline_tests.append(pipe) + + if len(updated_pipeline_tests) > 0: + test_map["pipelines"] = " ".join(sorted(updated_pipeline_tests)) + + with open(json_output_file, "w", encoding="UTF-8") as fp: + json.dump(test_map, fp, ensure_ascii=False) + + +def create_json_map(test_files_to_run: List[str], json_output_file: str | None = None): + """ + Creates a map from a list of tests to run to easily split them by category, when running parallelism of slow tests. + + Args: + test_files_to_run (`List[str]`): The list of tests to run. + json_output_file (`str`): The path where to store the built json map. + """ + if json_output_file is None: + return + + test_map = {} + for test_file in test_files_to_run: + # `test_file` is a path to a test folder/file, starting with `tests/`. For example, + # - `tests/models/bert/test_modeling_bert.py` or `tests/models/bert` + # - `tests/trainer/test_trainer.py` or `tests/trainer` + # - `tests/test_modeling_common.py` + names = test_file.split(os.path.sep) + module = names[1] + if module in MODULES_TO_IGNORE: + continue + + if len(names) > 2 or not test_file.endswith(".py"): + # test folders under `tests` or python files under them + # take the part like tokenization, `pipeline`, etc. for other test categories + key = os.path.sep.join(names[1:2]) + else: + # common test files directly under `tests/` + key = "common" + + if key not in test_map: + test_map[key] = [] + test_map[key].append(test_file) + + # sort the keys & values + keys = sorted(test_map.keys()) + test_map = {k: " ".join(sorted(test_map[k])) for k in keys} + + with open(json_output_file, "w", encoding="UTF-8") as fp: + json.dump(test_map, fp, ensure_ascii=False) + + +def infer_tests_to_run( + output_file: str, + diff_with_last_commit: bool = False, + json_output_file: str | None = None, +): + """ + The main function called by the test fetcher. Determines the tests to run from the diff. + + Args: + output_file (`str`): + The path where to store the summary of the test fetcher analysis. Other files will be stored in the same + folder: + + - examples_test_list.txt: The list of examples tests to run. + - test_repo_utils.txt: Will indicate if the repo utils tests should be run or not. + - doctest_list.txt: The list of doctests to run. + + diff_with_last_commit (`bool`, *optional*, defaults to `False`): + Whether to analyze the diff with the last commit (for use on the main branch after a PR is merged) or with + the branching point from main (for use on each PR). + filter_models (`bool`, *optional*, defaults to `True`): + Whether or not to filter the tests to core models only, when a file modified results in a lot of model + tests. + json_output_file (`str`, *optional*): + The path where to store the json file mapping categories of tests to tests to run (used for parallelism or + the slow tests). + """ + modified_files = get_modified_python_files(diff_with_last_commit=diff_with_last_commit) + print(f"\n### MODIFIED FILES ###\n{_print_list(modified_files)}") + # Create the map that will give us all impacted modules. + reverse_map = create_reverse_dependency_map() + impacted_files = modified_files.copy() + for f in modified_files: + if f in reverse_map: + impacted_files.extend(reverse_map[f]) + + # Remove duplicates + impacted_files = sorted(set(impacted_files)) + print(f"\n### IMPACTED FILES ###\n{_print_list(impacted_files)}") + + # Grab the corresponding test files: + if any(x in modified_files for x in ["setup.py"]): + test_files_to_run = ["tests", "examples"] + + # in order to trigger pipeline tests even if no code change at all + if "tests/utils/tiny_model_summary.json" in modified_files: + test_files_to_run = ["tests"] + any(f.split(os.path.sep)[0] == "utils" for f in modified_files) + else: + # All modified tests need to be run. + test_files_to_run = [ + f for f in modified_files if f.startswith("tests") and f.split(os.path.sep)[-1].startswith("test") + ] + # Then we grab the corresponding test files. + test_map = create_module_to_test_map(reverse_map=reverse_map) + for f in modified_files: + if f in test_map: + test_files_to_run.extend(test_map[f]) + test_files_to_run = sorted(set(test_files_to_run)) + # Make sure we did not end up with a test file that was removed + test_files_to_run = [f for f in test_files_to_run if (PATH_TO_REPO / f).exists()] + + any(f.split(os.path.sep)[0] == "utils" for f in modified_files) + + examples_tests_to_run = [f for f in test_files_to_run if f.startswith("examples")] + test_files_to_run = [f for f in test_files_to_run if not f.startswith("examples")] + print(f"\n### TEST TO RUN ###\n{_print_list(test_files_to_run)}") + if len(test_files_to_run) > 0: + with open(output_file, "w", encoding="utf-8") as f: + f.write(" ".join(test_files_to_run)) + + # Create a map that maps test categories to test files, i.e. `models/bert` -> [...test_modeling_bert.py, ...] + + # Get all test directories (and some common test files) under `tests` and `tests/models` if `test_files_to_run` + # contains `tests` (i.e. when `setup.py` is changed). + if "tests" in test_files_to_run: + test_files_to_run = get_all_tests() + + create_json_map(test_files_to_run, json_output_file) + + print(f"\n### EXAMPLES TEST TO RUN ###\n{_print_list(examples_tests_to_run)}") + if len(examples_tests_to_run) > 0: + # We use `all` in the case `commit_flags["test_all"]` as well as in `create_circleci_config.py` for processing + if examples_tests_to_run == ["examples"]: + examples_tests_to_run = ["all"] + example_file = Path(output_file).parent / "examples_test_list.txt" + with open(example_file, "w", encoding="utf-8") as f: + f.write(" ".join(examples_tests_to_run)) + + +def filter_tests(output_file: str, filters: List[str]): + """ + Reads the content of the output file and filters out all the tests in a list of given folders. + + Args: + output_file (`str` or `os.PathLike`): The path to the output file of the tests fetcher. + filters (`List[str]`): A list of folders to filter. + """ + if not os.path.isfile(output_file): + print("No test file found.") + return + with open(output_file, "r", encoding="utf-8") as f: + test_files = f.read().split(" ") + + if len(test_files) == 0 or test_files == [""]: + print("No tests to filter.") + return + + if test_files == ["tests"]: + test_files = [os.path.join("tests", f) for f in os.listdir("tests") if f not in ["__init__.py"] + filters] + else: + test_files = [f for f in test_files if f.split(os.path.sep)[1] not in filters] + + with open(output_file, "w", encoding="utf-8") as f: + f.write(" ".join(test_files)) + + +def parse_commit_message(commit_message: str) -> dict[str, bool]: + """ + Parses the commit message to detect if a command is there to skip, force all or part of the CI. + + Args: + commit_message (`str`): The commit message of the current commit. + + Returns: + `Dict[str, bool]`: A dictionary of strings to bools with keys the following keys: `"skip"`, + `"test_all_models"` and `"test_all"`. + """ + if commit_message is None: + return {"skip": False, "no_filter": False, "test_all": False} + + command_search = re.search(r"\[([^\]]*)\]", commit_message) + if command_search is not None: + command = command_search.groups()[0] + command = command.lower().replace("-", " ").replace("_", " ") + skip = command in ["ci skip", "skip ci", "circleci skip", "skip circleci"] + no_filter = set(command.split(" ")) == {"no", "filter"} + test_all = set(command.split(" ")) == {"test", "all"} + return {"skip": skip, "no_filter": no_filter, "test_all": test_all} + else: + return {"skip": False, "no_filter": False, "test_all": False} + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--output_file", type=str, default="test_list.txt", help="Where to store the list of tests to run" + ) + parser.add_argument( + "--json_output_file", + type=str, + default="test_map.json", + help="Where to store the tests to run in a dictionary format mapping test categories to test files", + ) + parser.add_argument( + "--diff_with_last_commit", + action="store_true", + help="To fetch the tests between the current commit and the last commit", + ) + parser.add_argument( + "--filter_tests", + action="store_true", + help="Will filter the pipeline/repo utils tests outside of the generated list of tests.", + ) + parser.add_argument( + "--print_dependencies_of", + type=str, + help="Will only print the tree of modules depending on the file passed.", + default=None, + ) + parser.add_argument( + "--commit_message", + type=str, + help="The commit message (which could contain a command to force all tests or skip the CI).", + default=None, + ) + args = parser.parse_args() + if args.print_dependencies_of is not None: + print_tree_deps_of(args.print_dependencies_of) + else: + repo = Repo(PATH_TO_REPO) + commit_message = repo.head.commit.message + commit_flags = parse_commit_message(commit_message) + if commit_flags["skip"]: + print("Force-skipping the CI") + quit() + if commit_flags["no_filter"]: + print("Running all tests fetched without filtering.") + if commit_flags["test_all"]: + print("Force-launching all tests") + + diff_with_last_commit = args.diff_with_last_commit + if not diff_with_last_commit and not repo.head.is_detached and repo.head.ref == repo.refs.main: + print("main branch detected, fetching tests against last commit.") + diff_with_last_commit = True + + if not commit_flags["test_all"]: + try: + infer_tests_to_run( + args.output_file, + diff_with_last_commit=diff_with_last_commit, + json_output_file=args.json_output_file, + ) + filter_tests(args.output_file, ["repo_utils"]) + update_test_map_with_core_pipelines(json_output_file=args.json_output_file) + + except Exception as e: + print(f"\nError when trying to grab the relevant tests: {e}\n\nRunning all tests.") + commit_flags["test_all"] = True + + if commit_flags["test_all"]: + with open(args.output_file, "w", encoding="utf-8") as f: + f.write("tests") + example_file = Path(args.output_file).parent / "examples_test_list.txt" + with open(example_file, "w", encoding="utf-8") as f: + f.write("all") + + test_files_to_run = get_all_tests() + create_json_map(test_files_to_run, args.json_output_file) + update_test_map_with_core_pipelines(json_output_file=args.json_output_file) diff --git a/tasks/tasksmith-deb681b2c96c/tests/source/utils/update_metadata.py b/tasks/tasksmith-deb681b2c96c/tests/source/utils/update_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..4fde581d4170ef46b168c4cbf59671463d9666c7 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/source/utils/update_metadata.py @@ -0,0 +1,125 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Utility that updates the metadata of the Diffusers library in the repository `huggingface/diffusers-metadata`. + +Usage for an update (as used by the GitHub action `update_metadata`): + +```bash +python utils/update_metadata.py +``` + +Script modified from: +https://github.com/huggingface/transformers/blob/main/utils/update_metadata.py +""" + +import argparse +import os +import tempfile + +import pandas as pd +from datasets import Dataset +from huggingface_hub import hf_hub_download, upload_folder + +from diffusers.pipelines.auto_pipeline import ( + AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, + AUTO_INPAINT_PIPELINES_MAPPING, + AUTO_TEXT2IMAGE_PIPELINES_MAPPING, +) + + +PIPELINE_TAG_JSON = "pipeline_tags.json" + + +def get_supported_pipeline_table() -> dict: + """ + Generates a dictionary containing the supported auto classes for each pipeline type, + using the content of the auto modules. + """ + # All supported pipelines for automatic mapping. + all_supported_pipeline_classes = [ + (class_name.__name__, "text-to-image", "AutoPipelineForText2Image") + for _, class_name in AUTO_TEXT2IMAGE_PIPELINES_MAPPING.items() + ] + all_supported_pipeline_classes += [ + (class_name.__name__, "image-to-image", "AutoPipelineForImage2Image") + for _, class_name in AUTO_IMAGE2IMAGE_PIPELINES_MAPPING.items() + ] + all_supported_pipeline_classes += [ + (class_name.__name__, "image-to-image", "AutoPipelineForInpainting") + for _, class_name in AUTO_INPAINT_PIPELINES_MAPPING.items() + ] + all_supported_pipeline_classes = list(set(all_supported_pipeline_classes)) + all_supported_pipeline_classes.sort(key=lambda x: x[0]) + + data = {} + data["pipeline_class"] = [sample[0] for sample in all_supported_pipeline_classes] + data["pipeline_tag"] = [sample[1] for sample in all_supported_pipeline_classes] + data["auto_class"] = [sample[2] for sample in all_supported_pipeline_classes] + + return data + + +def update_metadata(commit_sha: str): + """ + Update the metadata for the Diffusers repo in `huggingface/diffusers-metadata`. + + Args: + commit_sha (`str`): The commit SHA on Diffusers corresponding to this update. + """ + pipelines_table = get_supported_pipeline_table() + pipelines_table = pd.DataFrame(pipelines_table) + pipelines_dataset = Dataset.from_pandas(pipelines_table) + + hub_pipeline_tags_json = hf_hub_download( + repo_id="huggingface/diffusers-metadata", + filename=PIPELINE_TAG_JSON, + repo_type="dataset", + ) + with open(hub_pipeline_tags_json) as f: + hub_pipeline_tags_json = f.read() + + with tempfile.TemporaryDirectory() as tmp_dir: + pipelines_dataset.to_json(os.path.join(tmp_dir, PIPELINE_TAG_JSON)) + + with open(os.path.join(tmp_dir, PIPELINE_TAG_JSON)) as f: + pipeline_tags_json = f.read() + + hub_pipeline_tags_equal = hub_pipeline_tags_json == pipeline_tags_json + if hub_pipeline_tags_equal: + print("No updates, not pushing the metadata files.") + return + + if commit_sha is not None: + commit_message = ( + f"Update with commit {commit_sha}\n\nSee: https://github.com/huggingface/diffusers/commit/{commit_sha}" + ) + else: + commit_message = "Update" + + upload_folder( + repo_id="huggingface/diffusers-metadata", + folder_path=tmp_dir, + repo_type="dataset", + commit_message=commit_message, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--commit_sha", default=None, type=str, help="The sha of the commit going with this update.") + args = parser.parse_args() + + update_metadata(args.commit_sha) diff --git a/tasks/tasksmith-deb681b2c96c/tests/test.sh b/tasks/tasksmith-deb681b2c96c/tests/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..426176b8bea79061cb126a143199bafd3c1d6a41 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/test.sh @@ -0,0 +1,3 @@ +#!/bin/sh +set -eu +exec /usr/local/bin/python -I /tests/grade.py diff --git a/tasks/tasksmith-deb681b2c96c/tests/test_driver.py b/tasks/tasksmith-deb681b2c96c/tests/test_driver.py new file mode 100644 index 0000000000000000000000000000000000000000..c0b8bb331c4499e300b92f0e022ff6a723a4a562 --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/test_driver.py @@ -0,0 +1,41 @@ +"""Private pytest entry point with a bounded framework-tampering check. + +The host still owns reward parsing. This guard detects replacement or in-place +editing of unittest assertions, a demonstrated attack against repository tests. +It does not claim to isolate arbitrary test and solution code in one interpreter. +""" + +from __future__ import annotations + +import sys +import unittest + + +def run(arguments: list[str]) -> int: + import pytest + + # Capture before pytest imports any repository modules. Retain code objects + # too: an attacker can mutate function.__code__ without replacing a method. + original = { + name: (value, getattr(value, "__code__", None)) + for name, value in vars(unittest.TestCase).items() + if name.startswith("assert") and callable(value) + } + case = unittest.TestCase + # The harness supplies selectors and reporting explicitly. Repository addopts + # often enable optional coverage/report plugins that are intentionally not + # auto-loaded in this isolated verifier; they are not test dependencies. + code = int(pytest.main(["-o", "addopts=", *arguments])) + changed = [] + for name, (method, implementation) in original.items(): + current = getattr(case, name, None) + if current is not method or getattr(current, "__code__", None) is not implementation: + changed.append(name) + if unittest.TestCase is not case or changed: + sys.stderr.write("Verifier framework was modified: " + ", ".join(changed) + "\n") + return 254 + return code + + +if __name__ == "__main__": + raise SystemExit(run(sys.argv[1:])) diff --git a/tasks/tasksmith-deb681b2c96c/tests/test_results.py b/tasks/tasksmith-deb681b2c96c/tests/test_results.py new file mode 100644 index 0000000000000000000000000000000000000000..05ce2c730e38e02bad44f2344e88e5f1cb02cb8d --- /dev/null +++ b/tasks/tasksmith-deb681b2c96c/tests/test_results.py @@ -0,0 +1,65 @@ +"""Strict test evidence used by repository recipes and their exported verifiers.""" + +from __future__ import annotations + +from dataclasses import dataclass +from xml.etree import ElementTree + + +@dataclass(frozen=True) +class TestResults: + __test__ = False + statuses: dict[str, str] + returncode: int + + @property + def passed(self) -> set[str]: + return {name for name, status in self.statuses.items() if status == "passed"} + + +def parse_junit(text: str, *, returncode: int) -> TestResults: + if " dict[str, list[str]]: + if healthy.returncode != 0 or not healthy.passed: + raise ValueError("Reference must execute successfully with passing tests") + if healthy.statuses.keys() != defective.statuses.keys(): + raise ValueError("Mutation changed test collection; contrast is not comparable") + failing = sorted( + name for name in healthy.passed if defective.statuses[name] in {"failed", "error"} + ) + if not failing or defective.returncode != 1: + raise ValueError("No intended fail-to-pass contrast") + return {"FAIL_TO_PASS": failing, "PASS_TO_PASS": sorted(healthy.passed - set(failing))} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..4b58020f6edcd92793be88565cdf26573efa8bcb --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/Dockerfile @@ -0,0 +1,9 @@ +FROM python:3.12-slim +WORKDIR /workspace +RUN python -m pip install --no-cache-dir torch==2.11.0+cpu --extra-index-url https://download.pytorch.org/whl/cpu numpy==2.5.3 packaging==26.3 parameterized==0.9.0 pytest==8.4.2 setuptools==78.1.0 wheel==0.48.0 tokenizers==0.21.4 huggingface-hub==0.28.1 safetensors==0.5.3 regex==2026.9.10 requests==2.34.2 filelock==3.32.6 Pillow==12.3.0 tqdm==4.70.1 PyYAML==6.0.3 certifi==2026.7.22 charset-normalizer==3.5.1 idna==3.19 urllib3==2.7.0 typing_extensions==4.16.0 datasets==2.21.0 dill==0.3.8 multiprocess==0.70.16 xxhash==4.0.1 pyarrow==25.0.1 fsspec==2024.6.1 pandas==3.0.5 python-dateutil==2.9.0.post0 six==1.17.0 aiohappyeyeballs==2.7.1 aiohttp==3.14.3 aiosignal==1.4.0 attrs==26.1.0 frozenlist==1.8.0 multidict==6.8.0 propcache==0.5.2 yarl==1.24.5 +COPY source /workspace +RUN python -m pip install --no-cache-dir --no-deps --no-build-isolation -e . +ENV PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 +ENV HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 HF_DATASETS_OFFLINE=1 +RUN apt-get update && apt-get install -y --no-install-recommends tmux && rm -rf /var/lib/apt/lists/* +RUN useradd -m learner && chown -R learner:learner /workspace diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/TROUBLESHOOT.md b/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/TROUBLESHOOT.md new file mode 100644 index 0000000000000000000000000000000000000000..484d62b46a87f43e3df240823bd9227d8ebd11bc --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/TROUBLESHOOT.md @@ -0,0 +1,7 @@ +# Troubleshooting + +This is a document explaining how to deal with various issues on Circle-CI. The entries may include actual solutions or pointers to Issues that cover those. + +## Circle CI + +* pytest worker runs out of resident RAM and gets killed by `cgroups`: https://github.com/huggingface/transformers/issues/11408 diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/config.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..108e319d2cc72642f61f73265a992eba4953bbef --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/config.yml @@ -0,0 +1,224 @@ +version: 2.1 +setup: true +orbs: + continuation: circleci/continuation@0.1.0 + +parameters: + nightly: + type: boolean + default: false + +jobs: + # Ensure running with CircleCI/huggingface + check_circleci_user: + docker: + - image: python:3.10-slim + resource_class: small + parallelism: 1 + steps: + - run: echo $CIRCLE_PROJECT_USERNAME + - run: | + if [ "$CIRCLE_PROJECT_USERNAME" = "huggingface" ]; then + exit 0 + else + echo "The CI is running under $CIRCLE_PROJECT_USERNAME personal account. Please follow https://support.circleci.com/hc/en-us/articles/360008097173-Troubleshooting-why-pull-requests-are-not-triggering-jobs-on-my-organization- to fix it."; exit -1 + fi + # Fetch the tests to run + fetch_tests: + working_directory: ~/transformers + docker: + - image: huggingface/transformers-quality + parallelism: 1 + steps: + - checkout + - run: if [[ "$CIRCLE_PULL_REQUEST" == "" && "$CIRCLE_BRANCH" != "main" && "$CIRCLE_BRANCH" != *-release ]]; then echo "Not a PR, not the main branch and not a release branch, skip test!"; circleci-agent step halt; fi + - run: 'curl -L -H "Accept: application/vnd.github+json" -H "X-GitHub-Api-Version: 2022-11-28" https://api.github.com/repos/$CIRCLE_PROJECT_USERNAME/$CIRCLE_PROJECT_REPONAME/pulls/${CIRCLE_PULL_REQUEST##*/} >> github.txt' + - run: cat github.txt + - run: (python3 -c 'import json; from datetime import datetime; fp = open("github.txt"); data = json.load(fp); fp.close(); f = "%Y-%m-%dT%H:%M:%SZ"; created = datetime.strptime(data["created_at"], f); updated = datetime.strptime(data["updated_at"], f); s = (updated - created).total_seconds(); print(int(s))' || true) > elapsed.txt + - run: if [ "$(cat elapsed.txt)" == "" ]; then echo 60 > elapsed.txt; fi + - run: cat elapsed.txt + - run: if [ "$(cat elapsed.txt)" -lt "30" ]; then echo "PR is just opened, wait some actions from GitHub"; sleep 30; fi + - run: 'if grep -q "\"draft\": true," github.txt; then echo "draft mode, skip test!"; circleci-agent step halt; fi' + - run: uv pip install -U -e . + - run: echo 'export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)"' >> "$BASH_ENV" && source "$BASH_ENV" + - run: mkdir -p test_preparation + - run: python utils/tests_fetcher.py | tee tests_fetched_summary.txt + - run: python utils/tests_fetcher.py --filter_tests + - run: export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)" && echo $GIT_COMMIT_MESSAGE && python .circleci/create_circleci_config.py --fetcher_folder test_preparation + - run: | + if [ ! -s test_preparation/generated_config.yml ]; then + echo "No tests to run, exiting early!" + circleci-agent step halt + fi + + - store_artifacts: + path: test_preparation + + - run: + name: "Retrieve Artifact Paths" + # [reference] https://circleci.com/docs/api/v2/index.html#operation/getJobArtifacts + # `CIRCLE_TOKEN` is defined as an environment variables set within a context, see `https://circleci.com/docs/contexts/` + command: | + project_slug="gh/${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}" + job_number=${CIRCLE_BUILD_NUM} + url="https://circleci.com/api/v2/project/${project_slug}/${job_number}/artifacts" + curl -o test_preparation/artifacts.json ${url} --header "Circle-Token: $CIRCLE_TOKEN" + - run: + name: "Prepare pipeline parameters" + command: | + python utils/process_test_artifacts.py + + # To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters. + # Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation. + # We used: + + # https://circleci.com/docs/api/v2/index.html#operation/getJobArtifacts : to get the job artifacts + # We could not pass a nested dict, which is why we create the test_file_... parameters for every single job + + - store_artifacts: + path: test_preparation/transformed_artifacts.json + - store_artifacts: + path: test_preparation/artifacts.json + - continuation/continue: + parameters: test_preparation/transformed_artifacts.json + configuration_path: test_preparation/generated_config.yml + + # To run all tests for the nightly build + fetch_all_tests: + working_directory: ~/transformers + docker: + - image: huggingface/transformers-quality + parallelism: 1 + steps: + - checkout + - run: uv pip install -U -e . + - run: echo 'export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)"' >> "$BASH_ENV" && source "$BASH_ENV" + - run: mkdir -p test_preparation + - run: python utils/tests_fetcher.py --fetch_all | tee tests_fetched_summary.txt + - run: python utils/tests_fetcher.py --filter_tests + - run: export "GIT_COMMIT_MESSAGE=$(git show -s --format=%s)" && echo $GIT_COMMIT_MESSAGE && python .circleci/create_circleci_config.py --fetcher_folder test_preparation + - run: | + if [ ! -s test_preparation/generated_config.yml ]; then + echo "No tests to run, exiting early!" + circleci-agent step halt + fi + + - store_artifacts: + path: test_preparation + + - run: + name: "Retrieve Artifact Paths" + env: + CIRCLE_TOKEN: ${{ secrets.CI_ARTIFACT_TOKEN }} + command: | + project_slug="gh/${CIRCLE_PROJECT_USERNAME}/${CIRCLE_PROJECT_REPONAME}" + job_number=${CIRCLE_BUILD_NUM} + url="https://circleci.com/api/v2/project/${project_slug}/${job_number}/artifacts" + curl -o test_preparation/artifacts.json ${url} + - run: + name: "Prepare pipeline parameters" + command: | + python utils/process_test_artifacts.py + + # To avoid too long generated_config.yaml on the continuation orb, we pass the links to the artifacts as parameters. + # Otherwise the list of tests was just too big. Explicit is good but for that it was a limitation. + # We used: + + # https://circleci.com/docs/api/v2/index.html#operation/getJobArtifacts : to get the job artifacts + # We could not pass a nested dict, which is why we create the test_file_... parameters for every single job + + - store_artifacts: + path: test_preparation/transformed_artifacts.json + - store_artifacts: + path: test_preparation/artifacts.json + - continuation/continue: + parameters: test_preparation/transformed_artifacts.json + configuration_path: test_preparation/generated_config.yml + + check_code_quality: + working_directory: ~/transformers + docker: + - image: huggingface/transformers-quality + resource_class: large + environment: + TRANSFORMERS_IS_CI: yes + PYTEST_TIMEOUT: 120 + parallelism: 1 + steps: + - checkout + - run: uv pip install -e ".[quality]" + - run: + name: Show installed libraries and their versions + command: pip freeze | tee installed.txt + - store_artifacts: + path: ~/transformers/installed.txt + - run: python -c "from transformers import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) + - run: ruff check examples tests src utils + - run: ruff format tests src utils --check + - run: python utils/custom_init_isort.py --check_only + - run: python utils/sort_auto_mappings.py --check_only + - run: python utils/check_doc_toc.py + - run: python utils/check_docstrings.py --check_all + + check_repository_consistency: + working_directory: ~/transformers + docker: + - image: huggingface/transformers-consistency + resource_class: large + environment: + TRANSFORMERS_IS_CI: yes + PYTEST_TIMEOUT: 120 + parallelism: 1 + steps: + - checkout + - run: uv pip install -e ".[quality]" + - run: + name: Show installed libraries and their versions + command: pip freeze | tee installed.txt + - store_artifacts: + path: ~/transformers/installed.txt + - run: python utils/check_copies.py + - run: python utils/check_modular_conversion.py + - run: python utils/check_dummies.py + - run: python utils/check_repo.py + - run: python utils/check_inits.py + - run: python utils/check_config_docstrings.py + - run: python utils/check_config_attributes.py + - run: python utils/check_doctest_list.py + - run: make deps_table_check_updated + - run: python utils/update_metadata.py --check-only + - run: python utils/check_docstrings.py + +workflows: + version: 2 + setup_and_quality: + when: + and: + - equal: [<>, https://github.com/huggingface/transformers] + - not: <> + jobs: + - check_circleci_user + - check_code_quality + - check_repository_consistency + - fetch_tests + + setup_and_quality_2: + when: + not: + equal: [<>, https://github.com/huggingface/transformers] + jobs: + - check_circleci_user + - check_code_quality + - check_repository_consistency + - fetch_tests: + # [reference] https://circleci.com/docs/contexts/ + context: + - TRANSFORMERS_CONTEXT + + nightly: + when: <> + jobs: + - check_circleci_user + - check_code_quality + - check_repository_consistency + - fetch_all_tests diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/create_circleci_config.py b/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/create_circleci_config.py new file mode 100644 index 0000000000000000000000000000000000000000..156765d30f30a41331df433f819f6245ba7a2e31 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/create_circleci_config.py @@ -0,0 +1,389 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import copy +import os +import random +from dataclasses import dataclass +from typing import Any, Dict, List, Optional +import glob +import yaml + + +COMMON_ENV_VARIABLES = { + "OMP_NUM_THREADS": 1, + "TRANSFORMERS_IS_CI": True, + "PYTEST_TIMEOUT": 120, + "RUN_PIPELINE_TESTS": False, +} +# Disable the use of {"s": None} as the output is way too long, causing the navigation on CircleCI impractical +COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "dist": "loadfile", "vvv": None, "rsfE":None} +DEFAULT_DOCKER_IMAGE = [{"image": "cimg/python:3.8.12"}] + + +class EmptyJob: + job_name = "empty" + + def to_dict(self): + steps = [{"run": 'ls -la'}] + if self.job_name == "collection_job": + steps.extend( + [ + "checkout", + {"run": "pip install requests || true"}, + {"run": """while [[ $(curl --location --request GET "https://circleci.com/api/v2/workflow/$CIRCLE_WORKFLOW_ID/job" --header "Circle-Token: $CCI_TOKEN"| jq -r '.items[]|select(.name != "collection_job")|.status' | grep -c "running") -gt 0 ]]; do sleep 5; done || true"""}, + {"run": 'python utils/process_circleci_workflow_test_reports.py --workflow_id $CIRCLE_WORKFLOW_ID || true'}, + {"store_artifacts": {"path": "outputs"}}, + {"run": 'echo "All required jobs have now completed"'}, + ] + ) + + return { + "docker": copy.deepcopy(DEFAULT_DOCKER_IMAGE), + "resource_class": "small", + "steps": steps, + } + + +@dataclass +class CircleCIJob: + name: str + additional_env: Dict[str, Any] = None + docker_image: List[Dict[str, str]] = None + install_steps: List[str] = None + marker: Optional[str] = None + parallelism: Optional[int] = 0 + pytest_num_workers: int = 8 + pytest_options: Dict[str, Any] = None + resource_class: Optional[str] = "xlarge" + tests_to_run: Optional[List[str]] = None + num_test_files_per_worker: Optional[int] = 10 + # This should be only used for doctest job! + command_timeout: Optional[int] = None + + def __post_init__(self): + # Deal with defaults for mutable attributes. + if self.additional_env is None: + self.additional_env = {} + if self.docker_image is None: + # Let's avoid changing the default list and make a copy. + self.docker_image = copy.deepcopy(DEFAULT_DOCKER_IMAGE) + else: + # BIG HACK WILL REMOVE ONCE FETCHER IS UPDATED + print(os.environ.get("GIT_COMMIT_MESSAGE")) + if "[build-ci-image]" in os.environ.get("GIT_COMMIT_MESSAGE", "") or os.environ.get("GIT_COMMIT_MESSAGE", "") == "dev-ci": + self.docker_image[0]["image"] = f"{self.docker_image[0]['image']}:dev" + print(f"Using {self.docker_image} docker image") + if self.install_steps is None: + self.install_steps = ["uv venv && uv pip install ."] + if self.pytest_options is None: + self.pytest_options = {} + if isinstance(self.tests_to_run, str): + self.tests_to_run = [self.tests_to_run] + else: + test_file = os.path.join("test_preparation" , f"{self.job_name}_test_list.txt") + print("Looking for ", test_file) + if os.path.exists(test_file): + with open(test_file) as f: + expanded_tests = f.read().strip().split("\n") + self.tests_to_run = expanded_tests + print("Found:", expanded_tests) + else: + self.tests_to_run = [] + print("not Found") + + def to_dict(self): + env = COMMON_ENV_VARIABLES.copy() + env.update(self.additional_env) + + job = { + "docker": self.docker_image, + "environment": env, + } + if self.resource_class is not None: + job["resource_class"] = self.resource_class + + all_options = {**COMMON_PYTEST_OPTIONS, **self.pytest_options} + pytest_flags = [f"--{key}={value}" if (value is not None or key in ["doctest-modules"]) else f"-{key}" for key, value in all_options.items()] + pytest_flags.append( + f"--make-reports={self.name}" if "examples" in self.name else f"--make-reports=tests_{self.name}" + ) + # Examples special case: we need to download NLTK files in advance to avoid cuncurrency issues + timeout_cmd = f"timeout {self.command_timeout} " if self.command_timeout else "" + marker_cmd = f"-m '{self.marker}'" if self.marker is not None else "" + additional_flags = f" -p no:warning -o junit_family=xunit1 --junitxml=test-results/junit.xml" + parallel = f' << pipeline.parameters.{self.job_name}_parallelism >> ' + steps = [ + "checkout", + {"attach_workspace": {"at": "test_preparation"}}, + {"run": "apt-get update && apt-get install -y curl"}, + {"run": " && ".join(self.install_steps)}, + {"run": {"name": "Download NLTK files", "command": """python -c "import nltk; nltk.download('punkt', quiet=True)" """} if "example" in self.name else "echo Skipping"}, + {"run": { + "name": "Show installed libraries and their size", + "command": """du -h -d 1 "$(pip -V | cut -d ' ' -f 4 | sed 's/pip//g')" | grep -vE "dist-info|_distutils_hack|__pycache__" | sort -h | tee installed.txt || true"""} + }, + {"run": { + "name": "Show installed libraries and their versions", + "command": """pip list --format=freeze | tee installed.txt || true"""} + }, + {"run": { + "name": "Show biggest libraries", + "command": """dpkg-query --show --showformat='${Installed-Size}\t${Package}\n' | sort -rh | head -25 | sort -h | awk '{ package=$2; sub(".*/", "", package); printf("%.5f GB %s\n", $1/1024/1024, package)}' || true"""} + }, + {"run": {"name": "Create `test-results` directory", "command": "mkdir test-results"}}, + {"run": {"name": "Get files to test", "command":f'curl -L -o {self.job_name}_test_list.txt <> --header "Circle-Token: $CIRCLE_TOKEN"' if self.name != "pr_documentation_tests" else 'echo "Skipped"'}}, + {"run": {"name": "Split tests across parallel nodes: show current parallel tests", + "command": f"TESTS=$(circleci tests split --split-by=timings {self.job_name}_test_list.txt) && echo $TESTS > splitted_tests.txt && echo $TESTS | tr ' ' '\n'" if self.parallelism else f"awk '{{printf \"%s \", $0}}' {self.job_name}_test_list.txt > splitted_tests.txt" + } + }, + {"run": { + "name": "Run tests", + "command": f"({timeout_cmd} python3 -m pytest {marker_cmd} -n {self.pytest_num_workers} {additional_flags} {' '.join(pytest_flags)} $(cat splitted_tests.txt) | tee tests_output.txt)"} + }, + {"run": {"name": "Expand to show skipped tests", "when": "always", "command": f"python3 .circleci/parse_test_outputs.py --file tests_output.txt --skip"}}, + {"run": {"name": "Failed tests: show reasons", "when": "always", "command": f"python3 .circleci/parse_test_outputs.py --file tests_output.txt --fail"}}, + {"run": {"name": "Errors", "when": "always", "command": f"python3 .circleci/parse_test_outputs.py --file tests_output.txt --errors"}}, + {"store_test_results": {"path": "test-results"}}, + {"store_artifacts": {"path": "test-results/junit.xml"}}, + {"store_artifacts": {"path": "reports"}}, + {"store_artifacts": {"path": "tests.txt"}}, + {"store_artifacts": {"path": "splitted_tests.txt"}}, + {"store_artifacts": {"path": "installed.txt"}}, + ] + if self.parallelism: + job["parallelism"] = parallel + job["steps"] = steps + return job + + @property + def job_name(self): + return self.name if ("examples" in self.name or "pipeline" in self.name or "pr_documentation" in self.name) else f"tests_{self.name}" + + +# JOBS +torch_job = CircleCIJob( + "torch", + docker_image=[{"image": "huggingface/transformers-torch-light"}], + marker="not generate", + parallelism=6, +) + +generate_job = CircleCIJob( + "generate", + docker_image=[{"image": "huggingface/transformers-torch-light"}], + marker="generate", + parallelism=6, +) + +tokenization_job = CircleCIJob( + "tokenization", + docker_image=[{"image": "huggingface/transformers-torch-light"}], + parallelism=8, +) + +processor_job = CircleCIJob( + "processors", + docker_image=[{"image": "huggingface/transformers-torch-light"}], + parallelism=8, +) + +tf_job = CircleCIJob( + "tf", + docker_image=[{"image":"huggingface/transformers-tf-light"}], + parallelism=6, +) + + +flax_job = CircleCIJob( + "flax", + docker_image=[{"image":"huggingface/transformers-jax-light"}], + parallelism=6, + pytest_num_workers=16, + resource_class="2xlarge", +) + + +pipelines_torch_job = CircleCIJob( + "pipelines_torch", + additional_env={"RUN_PIPELINE_TESTS": True}, + docker_image=[{"image":"huggingface/transformers-torch-light"}], + marker="is_pipeline_test", + parallelism=4, +) + + +pipelines_tf_job = CircleCIJob( + "pipelines_tf", + additional_env={"RUN_PIPELINE_TESTS": True}, + docker_image=[{"image":"huggingface/transformers-tf-light"}], + marker="is_pipeline_test", + parallelism=4, +) + + +custom_tokenizers_job = CircleCIJob( + "custom_tokenizers", + additional_env={"RUN_CUSTOM_TOKENIZERS": True}, + docker_image=[{"image": "huggingface/transformers-custom-tokenizers"}], +) + + +examples_torch_job = CircleCIJob( + "examples_torch", + additional_env={"OMP_NUM_THREADS": 8}, + docker_image=[{"image":"huggingface/transformers-examples-torch"}], + # TODO @ArthurZucker remove this once docker is easier to build + install_steps=["uv venv && uv pip install . && uv pip install -r examples/pytorch/_tests_requirements.txt"], +) + + +examples_tensorflow_job = CircleCIJob( + "examples_tensorflow", + additional_env={"OMP_NUM_THREADS": 8}, + docker_image=[{"image":"huggingface/transformers-examples-tf"}], +) + + +hub_job = CircleCIJob( + "hub", + additional_env={"HUGGINGFACE_CO_STAGING": True}, + docker_image=[{"image":"huggingface/transformers-torch-light"}], + install_steps=[ + 'uv venv && uv pip install .', + 'git config --global user.email "ci@dummy.com"', + 'git config --global user.name "ci"', + ], + marker="is_staging_test", + pytest_num_workers=2, + resource_class="medium", +) + + +onnx_job = CircleCIJob( + "onnx", + docker_image=[{"image":"huggingface/transformers-torch-tf-light"}], + install_steps=[ + "uv venv", + "uv pip install .[torch,tf,testing,sentencepiece,onnxruntime,vision,rjieba]", + ], + pytest_options={"k onnx": None}, + pytest_num_workers=1, + resource_class="small", +) + + +exotic_models_job = CircleCIJob( + "exotic_models", + docker_image=[{"image":"huggingface/transformers-exotic-models"}], + parallelism=4, + pytest_options={"durations": 100}, +) + + +repo_utils_job = CircleCIJob( + "repo_utils", + docker_image=[{"image":"huggingface/transformers-consistency"}], + pytest_num_workers=4, + resource_class="large", +) + + +non_model_job = CircleCIJob( + "non_model", + docker_image=[{"image": "huggingface/transformers-torch-light"}], + marker="not generate", + parallelism=6, +) + + +# We also include a `dummy.py` file in the files to be doc-tested to prevent edge case failure. Otherwise, the pytest +# hangs forever during test collection while showing `collecting 0 items / 21 errors`. (To see this, we have to remove +# the bash output redirection.) +py_command = 'from utils.tests_fetcher import get_doctest_files; to_test = get_doctest_files() + ["dummy.py"]; to_test = " ".join(to_test); print(to_test)' +py_command = f"$(python3 -c '{py_command}')" +command = f'echo """{py_command}""" > pr_documentation_tests_temp.txt' +doc_test_job = CircleCIJob( + "pr_documentation_tests", + docker_image=[{"image":"huggingface/transformers-consistency"}], + additional_env={"TRANSFORMERS_VERBOSITY": "error", "DATASETS_VERBOSITY": "error", "SKIP_CUDA_DOCTEST": "1"}, + install_steps=[ + # Add an empty file to keep the test step running correctly even no file is selected to be tested. + "uv venv && pip install .", + "touch dummy.py", + command, + "cat pr_documentation_tests_temp.txt", + "tail -n1 pr_documentation_tests_temp.txt | tee pr_documentation_tests_test_list.txt" + ], + tests_to_run="$(cat pr_documentation_tests.txt)", # noqa + pytest_options={"-doctest-modules": None, "doctest-glob": "*.md", "dist": "loadfile", "rvsA": None}, + command_timeout=1200, # test cannot run longer than 1200 seconds + pytest_num_workers=1, +) + +REGULAR_TESTS = [torch_job, tf_job, flax_job, hub_job, onnx_job, tokenization_job, processor_job, generate_job, non_model_job] # fmt: skip +EXAMPLES_TESTS = [examples_torch_job, examples_tensorflow_job] +PIPELINE_TESTS = [pipelines_torch_job, pipelines_tf_job] +REPO_UTIL_TESTS = [repo_utils_job] +DOC_TESTS = [doc_test_job] +ALL_TESTS = REGULAR_TESTS + EXAMPLES_TESTS + PIPELINE_TESTS + REPO_UTIL_TESTS + DOC_TESTS + [custom_tokenizers_job] + [exotic_models_job] # fmt: skip + + +def create_circleci_config(folder=None): + if folder is None: + folder = os.getcwd() + os.environ["test_preparation_dir"] = folder + jobs = [k for k in ALL_TESTS if os.path.isfile(os.path.join("test_preparation" , f"{k.job_name}_test_list.txt") )] + print("The following jobs will be run ", jobs) + + if len(jobs) == 0: + jobs = [EmptyJob()] + else: + print("Full list of job name inputs", {j.job_name + "_test_list":{"type":"string", "default":''} for j in jobs}) + # Add a job waiting all the test jobs and aggregate their test summary files at the end + collection_job = EmptyJob() + collection_job.job_name = "collection_job" + jobs = [collection_job] + jobs + + config = { + "version": "2.1", + "parameters": { + # Only used to accept the parameters from the trigger + "nightly": {"type": "boolean", "default": False}, + "tests_to_run": {"type": "string", "default": ''}, + **{j.job_name + "_test_list":{"type":"string", "default":''} for j in jobs}, + **{j.job_name + "_parallelism":{"type":"integer", "default":1} for j in jobs}, + }, + "jobs": {j.job_name: j.to_dict() for j in jobs} + } + if "CIRCLE_TOKEN" in os.environ: + # For private forked repo. (e.g. new model addition) + config["workflows"] = {"version": 2, "run_tests": {"jobs": [{j.job_name: {"context": ["TRANSFORMERS_CONTEXT"]}} for j in jobs]}} + else: + # For public repo. (e.g. `transformers`) + config["workflows"] = {"version": 2, "run_tests": {"jobs": [j.job_name for j in jobs]}} + with open(os.path.join(folder, "generated_config.yml"), "w") as f: + f.write(yaml.dump(config, sort_keys=False, default_flow_style=False).replace("' << pipeline", " << pipeline").replace(">> '", " >>")) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--fetcher_folder", type=str, default=None, help="Only test that all tests and modules are accounted for." + ) + args = parser.parse_args() + + create_circleci_config(args.fetcher_folder) diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/parse_test_outputs.py b/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/parse_test_outputs.py new file mode 100644 index 0000000000000000000000000000000000000000..a69da1a3eafb27f37ee7115472aad11b8736690b --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.circleci/parse_test_outputs.py @@ -0,0 +1,70 @@ +import re +import argparse + +def parse_pytest_output(file_path): + skipped_tests = {} + skipped_count = 0 + with open(file_path, 'r') as file: + for line in file: + match = re.match(r'^SKIPPED \[(\d+)\] (tests/.*): (.*)$', line) + if match: + skipped_count += 1 + test_file, test_line, reason = match.groups() + skipped_tests[reason] = skipped_tests.get(reason, []) + [(test_file, test_line)] + for k,v in sorted(skipped_tests.items(), key=lambda x:len(x[1])): + print(f"{len(v):4} skipped because: {k}") + print("Number of skipped tests:", skipped_count) + +def parse_pytest_failure_output(file_path): + failed_tests = {} + failed_count = 0 + with open(file_path, 'r') as file: + for line in file: + match = re.match(r'^FAILED (tests/.*) - (.*): (.*)$', line) + if match: + failed_count += 1 + _, error, reason = match.groups() + failed_tests[reason] = failed_tests.get(reason, []) + [error] + for k,v in sorted(failed_tests.items(), key=lambda x:len(x[1])): + print(f"{len(v):4} failed because `{v[0]}` -> {k}") + print("Number of failed tests:", failed_count) + if failed_count>0: + exit(1) + +def parse_pytest_errors_output(file_path): + print(file_path) + error_tests = {} + error_count = 0 + with open(file_path, 'r') as file: + for line in file: + match = re.match(r'^ERROR (tests/.*) - (.*): (.*)$', line) + if match: + error_count += 1 + _, test_error, reason = match.groups() + error_tests[reason] = error_tests.get(reason, []) + [test_error] + for k,v in sorted(error_tests.items(), key=lambda x:len(x[1])): + print(f"{len(v):4} errored out because of `{v[0]}` -> {k}") + print("Number of errors:", error_count) + if error_count>0: + exit(1) + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--file", help="file to parse") + parser.add_argument("--skip", action="store_true", help="show skipped reasons") + parser.add_argument("--fail", action="store_true", help="show failed tests") + parser.add_argument("--errors", action="store_true", help="show failed tests") + args = parser.parse_args() + + if args.skip: + parse_pytest_output(args.file) + + if args.fail: + parse_pytest_failure_output(args.file) + + if args.errors: + parse_pytest_errors_output(args.file) + + +if __name__ == "__main__": + main() diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.gitattributes b/tasks/tasksmith-e0f68dd4420c/environment/source/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..6505bc7edf9af5694cee1a93b90c565872fbbeb9 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.gitattributes @@ -0,0 +1,4 @@ +*.py eol=lf +*.rst eol=lf +*.md eol=lf +*.mdx eol=lf \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/bug-report.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/bug-report.yml new file mode 100644 index 0000000000000000000000000000000000000000..9b2c00bac50d6db2f437fe568b48dc5eb050b6de --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/bug-report.yml @@ -0,0 +1,128 @@ +name: "\U0001F41B Bug Report" +description: Submit a bug report to help us improve transformers +labels: [ "bug" ] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! 🤗 + + Before you submit your bug report: + + - If it is your first time submitting, be sure to check our [bug report guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#did-you-find-a-bug) + - Try our [docs bot](https://huggingface.co/spaces/huggingchat/hf-docs-chat) -- it might be able to help you with your issue + + - type: textarea + id: system-info + attributes: + label: System Info + description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below. + placeholder: transformers version, platform, python version, ... + validations: + required: true + + - type: textarea + id: who-can-help + attributes: + label: Who can help? + description: | + Your issue will be replied to more quickly if you can figure out the right person to tag with @ + If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**. + + All issues are read by one of the core maintainers, so if you don't know who to tag, just leave this blank and + a core maintainer will ping the right person. + + Please tag fewer than 3 people. + + Models: + + - text models: @ArthurZucker + - vision models: @amyeroberts, @qubvel + - speech models: @ylacombe, @eustlb + - graph models: @clefourrier + + Library: + + - flax: @sanchit-gandhi + - generate: @zucchini-nlp (visual-language models) or @gante (all others) + - pipelines: @Rocketknight1 + - tensorflow: @gante and @Rocketknight1 + - tokenizers: @ArthurZucker and @itazap + - trainer: @muellerzr @SunMarc + + Integrations: + + - deepspeed: HF Trainer/Accelerate: @muellerzr + - ray/raytune: @richardliaw, @amogkam + - Big Model Inference: @SunMarc + - quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber + + Documentation: @stevhliu + + Model hub: + + - for issues with a model, report at https://discuss.huggingface.co/ and tag the model's creator. + + HF projects: + + - accelerate: [different repo](https://github.com/huggingface/accelerate) + - datasets: [different repo](https://github.com/huggingface/datasets) + - diffusers: [different repo](https://github.com/huggingface/diffusers) + - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers) + + Maintained examples (not research project or legacy): + + - Flax: @sanchit-gandhi + - PyTorch: See Models above and tag the person corresponding to the modality of the example. + - TensorFlow: @Rocketknight1 + + Research projects are not maintained and should be taken as is. + + placeholder: "@Username ..." + + - type: checkboxes + id: information-scripts-examples + attributes: + label: Information + description: 'The problem arises when using:' + options: + - label: "The official example scripts" + - label: "My own modified scripts" + + - type: checkboxes + id: information-tasks + attributes: + label: Tasks + description: "The tasks I am working on are:" + options: + - label: "An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)" + - label: "My own task or dataset (give details below)" + + - type: textarea + id: reproduction + validations: + required: true + attributes: + label: Reproduction + description: | + Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet. + Please include relevant config information with your code, for example your Trainers, TRL, Peft, and DeepSpeed configs. + If you have code snippets, error messages, stack traces please provide them here as well. + Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting + Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code. + + placeholder: | + Steps to reproduce the behavior: + + 1. + 2. + 3. + + + - type: textarea + id: expected-behavior + validations: + required: true + attributes: + label: Expected behavior + description: "A clear and concise description of what you would expect to happen." diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/config.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..1b9386d11221fc6be29a88ee0b77e768a4d5eb9d --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,12 @@ +blank_issues_enabled: true +version: 2.1 +contact_links: + - name: Model checkpoints on the Hugging Face Hub + url: https://huggingface.co/models + about: Open a Pull request / Discussion related to a specific model checkpoint directly on the Hugging Face Hub + - name: Website Related + url: https://github.com/huggingface/hub-docs/issues + about: Feature requests and bug reports related to the website + - name: Forum + url: https://discuss.huggingface.co/ + about: General usage questions and community discussions diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/feature-request.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/feature-request.yml new file mode 100644 index 0000000000000000000000000000000000000000..ff0d452a807f6e12958b8e91dd8f18bcbf7962a4 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/feature-request.yml @@ -0,0 +1,31 @@ +name: "\U0001F680 Feature request" +description: Submit a proposal/request for a new transformers feature +labels: [ "Feature request" ] +body: + - type: textarea + id: feature-request + validations: + required: true + attributes: + label: Feature request + description: | + A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. + + - type: textarea + id: motivation + validations: + required: true + attributes: + label: Motivation + description: | + Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too. + + + - type: textarea + id: contribution + validations: + required: true + attributes: + label: Your contribution + description: | + Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/i18n.md b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/i18n.md new file mode 100644 index 0000000000000000000000000000000000000000..5b91427d55b73c616d9320cefbecf61787c51c1e --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/i18n.md @@ -0,0 +1,46 @@ +--- +name: 🌐 Translating a new language? +about: Start a new translation effort in your language +title: '[i18n-] Translating docs to ' +labels: WIP +assignees: '' + +--- + + + +Hi! + +Let's bring the documentation to all the -speaking community 🌐 (currently 0 out of 267 complete) + +Who would want to translate? Please follow the 🤗 [TRANSLATING guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list. + +Some notes: + +* Please translate using an informal tone (imagine you are talking with a friend about transformers 🤗). +* Please translate in a gender-neutral way. +* Add your translations to the folder called `` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source). +* Register your translation in `/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml). +* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu and @MKhalusova for review. +* 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/). + +## Get Started section + +- [ ] [index.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/index.md) https://github.com/huggingface/transformers/pull/20180 +- [ ] [quicktour.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.md) (waiting for initial PR to go through) +- [ ] [installation.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/installation.md). + +## Tutorial section +- [ ] [pipeline_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/pipeline_tutorial.md) +- [ ] [autoclass_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/autoclass_tutorial.md) +- [ ] [preprocessing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/preprocessing.md) +- [ ] [training.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/training.md) +- [ ] [accelerate.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/accelerate.md) +- [ ] [model_sharing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_sharing.md) +- [ ] [multilingual.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/multilingual.md) + + diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/migration.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/migration.yml new file mode 100644 index 0000000000000000000000000000000000000000..778413141b1f3b42f71c123a4d83f61bc01663e1 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/migration.yml @@ -0,0 +1,72 @@ +name: "\U0001F4DA Migration from pytorch-pretrained-bert or pytorch-transformers" +description: Report a problem when migrating from pytorch-pretrained-bert or pytorch-transformers to transformers +labels: [ "migration" ] +body: + - type: textarea + id: system-info + attributes: + label: System Info + description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below. + render: shell + placeholder: transformers version, platform, python version, ... + validations: + required: true + + - type: checkboxes + id: information-scripts-examples + attributes: + label: Information + description: 'The problem arises when using:' + options: + - label: "The official example scripts" + - label: "My own modified scripts" + + - type: checkboxes + id: information-tasks + attributes: + label: Tasks + description: "The tasks I am working on are:" + options: + - label: "An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)" + - label: "My own task or dataset (give details below)" + + - type: textarea + id: reproduction + validations: + required: true + attributes: + label: Reproduction + description: | + Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet. + If you have code snippets, error messages, stack traces please provide them here as well. + Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting + Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code. + + placeholder: | + Steps to reproduce the behavior: + + 1. + 2. + 3. + + + - type: textarea + id: expected-behavior + validations: + required: true + attributes: + label: Expected behavior + description: "A clear and concise description of what you would expect to happen." + render: shell + + - type: checkboxes + id: checklist + attributes: + label: Checklist + options: + - label: "I have read the migration guide in the readme. + ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers); + [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))" + required: true + - label: "I checked if a related official extension example runs on my machine." + required: true diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/new-model-addition.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/new-model-addition.yml new file mode 100644 index 0000000000000000000000000000000000000000..2f3476d3ab095ffb109b2b498247a0984cd0b500 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/ISSUE_TEMPLATE/new-model-addition.yml @@ -0,0 +1,31 @@ +name: "\U0001F31F New model addition" +description: Submit a proposal/request to implement a new model +labels: [ "New model" ] + +body: + - type: textarea + id: description-request + validations: + required: true + attributes: + label: Model description + description: | + Put any and all important information relative to the model + + - type: checkboxes + id: information-tasks + attributes: + label: Open source status + description: | + Please note that if the model implementation isn't available or if the weights aren't open-source, we are less likely to implement it in `transformers`. + options: + - label: "The model implementation is available" + - label: "The model weights are available" + + - type: textarea + id: additional-info + attributes: + label: Provide useful links for the implementation + description: | + Please provide information regarding the implementation, the weights, and the authors. + Please mention the authors by @gh-username if you're aware of their usernames. diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/PULL_REQUEST_TEMPLATE.md b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000000000000000000000000000000000000..ee7a7eaae1139bd7497bcc1becae6cfd27ff4479 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,79 @@ +# What does this PR do? + + + + + +Fixes # (issue) + + +## Before submitting +- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). +- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request), + Pull Request section? +- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link + to it if that's the case. +- [ ] Did you make sure to update the documentation with your changes? Here are the + [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and + [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). +- [ ] Did you write any new necessary tests? + + +## Who can review? + +Anyone in the community is free to review the PR once the tests have passed. Feel free to tag +members/contributors who may be interested in your PR. + + diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/conda/build.sh b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/conda/build.sh new file mode 100644 index 0000000000000000000000000000000000000000..a40f1097a8631648d5c2cecb1be4dd144cc632c2 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/conda/build.sh @@ -0,0 +1 @@ +$PYTHON setup.py install # Python command to install the script. diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/conda/meta.yaml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/conda/meta.yaml new file mode 100644 index 0000000000000000000000000000000000000000..89dc353b127774528faa0c979e28215be12de6f0 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/conda/meta.yaml @@ -0,0 +1,56 @@ +{% set name = "transformers" %} + +package: + name: "{{ name|lower }}" + version: "{{ TRANSFORMERS_VERSION }}" + +source: + path: ../../ + +build: + noarch: python + +requirements: + host: + - python + - pip + - numpy >=1.17 + - dataclasses + - huggingface_hub + - packaging + - filelock + - requests + - tqdm >=4.27 + - sacremoses + - regex !=2019.12.17 + - protobuf + - tokenizers >=0.11.1,!=0.11.3,<0.13 + - pyyaml >=5.1 + - safetensors + - fsspec + run: + - python + - numpy >=1.17 + - dataclasses + - huggingface_hub + - packaging + - filelock + - requests + - tqdm >=4.27 + - sacremoses + - regex !=2019.12.17 + - protobuf + - tokenizers >=0.11.1,!=0.11.3,<0.13 + - pyyaml >=5.1 + - safetensors + - fsspec + +test: + imports: + - transformers + +about: + home: https://huggingface.co + license: Apache License 2.0 + license_file: LICENSE + summary: "🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0." diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/TROUBLESHOOT.md b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/TROUBLESHOOT.md new file mode 100644 index 0000000000000000000000000000000000000000..f6101e6d70b59af8ef1878c1461c914090f9fa22 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/TROUBLESHOOT.md @@ -0,0 +1,9 @@ +# Troubleshooting + +This is a document explaining how to deal with various issues on github-actions self-hosted CI. The entries may include actual solutions or pointers to Issues that cover those. + +## GitHub Actions (self-hosted CI) + +* Deepspeed + + - if jit build hangs, clear out `rm -rf ~/.cache/torch_extensions/` reference: https://github.com/huggingface/transformers/pull/12723 diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/add-model-like.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/add-model-like.yml new file mode 100644 index 0000000000000000000000000000000000000000..cd6768317844065146ed425701f59cb87aec61bf --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/add-model-like.yml @@ -0,0 +1,80 @@ +name: Add model like runner + +on: + push: + branches: + - none # put main here when this is fixed + #pull_request: + # paths: + # - "src/**" + # - "tests/**" + # - ".github/**" + # types: [opened, synchronize, reopened] + +jobs: + run_tests_templates_like: + name: "Add new model like template tests" + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v4 + + - name: Install dependencies + run: | + sudo apt -y update && sudo apt install -y libsndfile1-dev + + - name: Load cached virtual environment + uses: actions/cache@v4 + id: cache + with: + path: ~/venv/ + key: v4-tests_model_like-${{ hashFiles('setup.py') }} + + - name: Create virtual environment on cache miss + if: steps.cache.outputs.cache-hit != 'true' + run: | + python -m venv ~/venv && . ~/venv/bin/activate + pip install --upgrade pip!=21.3 + pip install -e .[dev] + + - name: Check transformers location + # make `transformers` available as package (required since we use `-e` flag) and check it's indeed from the repo. + run: | + . ~/venv/bin/activate + python setup.py develop + transformers_install=$(pip list -e | grep transformers) + transformers_install_array=($transformers_install) + transformers_loc=${transformers_install_array[-1]} + transformers_repo_loc=$(pwd .) + if [ "$transformers_loc" != "$transformers_repo_loc" ]; then + echo "transformers is from $transformers_loc but it shoud be from $transformers_repo_loc/src." + echo "A fix is required. Stop testing." + exit 1 + fi + + - name: Create model files + run: | + . ~/venv/bin/activate + transformers-cli add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo . + make style + make fix-copies + + - name: Run all PyTorch modeling test + run: | + . ~/venv/bin/activate + python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_new_models tests/bert_new/test_modeling_bert_new.py + + - name: Run style changes + run: | + . ~/venv/bin/activate + make style && make quality && make repo-consistency + + - name: Failure short reports + if: ${{ always() }} + run: cat reports/tests_new_models/failures_short.txt + + - name: Test suite reports artifacts + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: run_all_tests_new_models_test_reports + path: reports/tests_new_models diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/benchmark.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/benchmark.yml new file mode 100644 index 0000000000000000000000000000000000000000..6b5555097c097bdc8940e31b438263e8e8af4377 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/benchmark.yml @@ -0,0 +1,76 @@ +name: Self-hosted runner (benchmark) + +on: + push: + branches: [main] + pull_request: + types: [ opened, labeled, reopened, synchronize ] + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +env: + HF_HOME: /mnt/cache + +jobs: + benchmark: + name: Benchmark + strategy: + matrix: + # group: [aws-g5-4xlarge-cache, aws-p4d-24xlarge-plus] (A100 runner is not enabled) + group: [aws-g5-4xlarge-cache] + runs-on: + group: ${{ matrix.group }} + if: | + (github.event_name == 'pull_request' && contains( github.event.pull_request.labels.*.name, 'run-benchmark') )|| + (github.event_name == 'push' && github.ref == 'refs/heads/main') + container: + image: huggingface/transformers-pytorch-gpu + options: --gpus all --privileged --ipc host + steps: + - name: Get repo + uses: actions/checkout@v4 + with: + ref: ${{ github.event.pull_request.head.sha || github.sha }} + + - name: Install libpq-dev & psql + run: | + apt update + apt install -y libpq-dev postgresql-client + + - name: Install benchmark script dependencies + run: python3 -m pip install -r benchmark/requirements.txt + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e ".[torch]" + + - name: Run database init script + run: | + psql -f benchmark/init_db.sql + env: + PGDATABASE: metrics + PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }} + PGUSER: transformers_benchmarks + PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }} + + - name: Run benchmark + run: | + git config --global --add safe.directory /__w/transformers/transformers + if [ "$GITHUB_EVENT_NAME" = "pull_request" ]; then + commit_id=$(echo "${{ github.event.pull_request.head.sha }}") + elif [ "$GITHUB_EVENT_NAME" = "push" ]; then + commit_id=$GITHUB_SHA + fi + commit_msg=$(git show -s --format=%s | cut -c1-70) + python3 benchmark/benchmarks_entrypoint.py "$BRANCH_NAME" "$commit_id" "$commit_msg" + env: + HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + # Enable this to see debug logs + # HF_HUB_VERBOSITY: debug + # TRANSFORMERS_VERBOSITY: debug + PGHOST: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGHOST }} + PGUSER: transformers_benchmarks + PGPASSWORD: ${{ secrets.TRANSFORMERS_BENCHMARKS_PGPASSWORD }} + BRANCH_NAME: ${{ github.head_ref || github.ref_name }} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-ci-docker-images.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-ci-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..5606668531da95a9839921e7a79be0f81d2191a3 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-ci-docker-images.yml @@ -0,0 +1,77 @@ +name: Build pr ci-docker + +on: + push: + branches: + - push-ci-image # for now let's only build on this branch + repository_dispatch: + workflow_call: + inputs: + image_postfix: + required: true + type: string + schedule: + - cron: "6 0 * * *" + + +concurrency: + group: ${{ github.workflow }} + cancel-in-progress: true + +jobs: + build: + runs-on: ubuntu-22.04 + + if: ${{ contains(github.event.head_commit.message, '[build-ci-image]') || contains(github.event.head_commit.message, '[push-ci-image]') && '!cancelled()' || github.event_name == 'schedule' }} + + strategy: + matrix: + file: ["quality", "consistency", "custom-tokenizers", "torch-light", "tf-light", "exotic-models", "torch-tf-light", "jax-light", "examples-torch", "examples-tf"] + continue-on-error: true + + steps: + - + name: Set tag + run: | + if ${{contains(github.event.head_commit.message, '[build-ci-image]')}}; then + echo "TAG=huggingface/transformers-${{ matrix.file }}:dev" >> "$GITHUB_ENV" + echo "setting it to DEV!" + else + echo "TAG=huggingface/transformers-${{ matrix.file }}" >> "$GITHUB_ENV" + + fi + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build ${{ matrix.file }}.dockerfile + uses: docker/build-push-action@v5 + with: + context: ./docker + build-args: | + REF=${{ github.sha }} + file: "./docker/${{ matrix.file }}.dockerfile" + push: ${{ contains(github.event.head_commit.message, 'ci-image]') || github.event_name == 'schedule' }} + tags: ${{ env.TAG }} + + notify: + runs-on: ubuntu-22.04 + if: ${{ contains(github.event.head_commit.message, '[build-ci-image]') || contains(github.event.head_commit.message, '[push-ci-image]') && '!cancelled()' || github.event_name == 'schedule' }} + steps: + - name: Post to Slack + if: ${{ contains(github.event.head_commit.message, '[push-ci-image]') && github.event_name != 'schedule' }} + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: "#transformers-ci-circleci-images" + title: 🤗 New docker images for CircleCI are pushed. + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-docker-images.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..c21faf2d7479426bd7c995e018cef935446ba3be --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-docker-images.yml @@ -0,0 +1,393 @@ +name: Build docker images (scheduled) + +on: + push: + branches: + - build_ci_docker_image* + repository_dispatch: + workflow_call: + inputs: + image_postfix: + required: true + type: string + schedule: + - cron: "17 0 * * *" + +concurrency: + group: docker-images-builds + cancel-in-progress: false + +jobs: + latest-docker: + name: "Latest PyTorch + TensorFlow [dev]" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-all-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-all-latest-gpu${{ inputs.image_postfix }} + # Push CI images still need to be re-built daily + - + name: Build and push (for Push CI) in a daily basis + # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. + # The later case is useful for manual image building for debugging purpose. Use another tag in this case! + if: inputs.image_postfix != '-push-ci' + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-all-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-all-latest-gpu-push-ci + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the transformers-all-latest-gpu-push-ci docker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-torch-deepspeed-docker: + name: "Latest PyTorch + DeepSpeed" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-deepspeed-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-latest-gpu${{ inputs.image_postfix }} + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER}} + title: 🤗 Results of the transformers-pytorch-deepspeed-latest-gpu docker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + # Can't build 2 images in a single job `latest-torch-deepspeed-docker` (for `nvcr.io/nvidia`) + latest-torch-deepspeed-docker-for-push-ci-daily-build: + name: "Latest PyTorch + DeepSpeed (Push CI - Daily Build)" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + # Push CI images still need to be re-built daily + - + name: Build and push (for Push CI) in a daily basis + # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. + # The later case is useful for manual image building for debugging purpose. Use another tag in this case! + if: inputs.image_postfix != '-push-ci' + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-deepspeed-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the transformers-pytorch-deepspeed-latest-gpu-push-ci docker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + doc-builder: + name: "Doc builder" + # Push CI doesn't need this image + if: inputs.image_postfix != '-push-ci' + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-doc-builder + push: true + tags: huggingface/transformers-doc-builder + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the huggingface/transformers-doc-builder docker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-pytorch: + name: "Latest PyTorch [dev]" + # Push CI doesn't need this image + if: inputs.image_postfix != '-push-ci' + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-gpu + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the huggingface/transformers-pytorch-gpudocker build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-pytorch-amd: + name: "Latest PyTorch (AMD) [dev]" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-amd-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-amd-gpu${{ inputs.image_postfix }} + # Push CI images still need to be re-built daily + - + name: Build and push (for Push CI) in a daily basis + # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. + # The later case is useful for manual image building for debugging purpose. Use another tag in this case! + if: inputs.image_postfix != '-push-ci' + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-amd-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-amd-gpu-push-ci + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the huggingface/transformers-pytorch-amd-gpu-push-ci build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-tensorflow: + name: "Latest TensorFlow [dev]" + # Push CI doesn't need this image + if: inputs.image_postfix != '-push-ci' + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-tensorflow-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-tensorflow-gpu + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the huggingface/transformers-tensorflow-gpu build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-pytorch-deepspeed-amd: + name: "PyTorch + DeepSpeed (AMD) [dev]" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-deepspeed-amd-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-amd-gpu${{ inputs.image_postfix }} + # Push CI images still need to be re-built daily + - + name: Build and push (for Push CI) in a daily basis + # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`. + # The later case is useful for manual image building for debugging purpose. Use another tag in this case! + if: inputs.image_postfix != '-push-ci' + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-pytorch-deepspeed-amd-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-amd-gpu-push-ci + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the transformers-pytorch-deepspeed-amd-gpu build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + + latest-quantization-torch-docker: + name: "Latest Pytorch + Quantization [dev]" + # Push CI doesn't need this image + if: inputs.image_postfix != '-push-ci' + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v5 + with: + context: ./docker/transformers-quantization-latest-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-quantization-latest-gpu${{ inputs.image_postfix }} + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ secrets.CI_SLACK_CHANNEL_DOCKER }} + title: 🤗 Results of the transformers-quantization-latest-gpu build + status: ${{ job.status }} + slack_token: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-nightly-ci-docker-images.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-nightly-ci-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..4b00a6d3fae36639fd2bf3a14b5517fdac0d1665 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-nightly-ci-docker-images.yml @@ -0,0 +1,67 @@ +name: Build docker images (Nightly CI) + +on: + workflow_call: + push: + branches: + - build_nightly_ci_docker_image* + +concurrency: + group: docker-images-builds + cancel-in-progress: false + +jobs: + latest-with-torch-nightly-docker: + name: "Nightly PyTorch + Stable TensorFlow" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v3 + with: + context: ./docker/transformers-all-latest-gpu + build-args: | + REF=main + PYTORCH=pre + push: true + tags: huggingface/transformers-all-latest-torch-nightly-gpu + + nightly-torch-deepspeed-docker: + name: "Nightly PyTorch + DeepSpeed" + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Check out code + uses: actions/checkout@v4 + - + name: Login to DockerHub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v3 + with: + context: ./docker/transformers-pytorch-deepspeed-nightly-gpu + build-args: | + REF=main + push: true + tags: huggingface/transformers-pytorch-deepspeed-nightly-gpu diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-past-ci-docker-images.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-past-ci-docker-images.yml new file mode 100644 index 0000000000000000000000000000000000000000..c4f0b78986caea24724dfa38465b1e27b88be335 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build-past-ci-docker-images.yml @@ -0,0 +1,101 @@ +name: Build docker images (Past CI) + +on: + push: + branches: + - build_past_ci_docker_image* + +concurrency: + group: docker-images-builds + cancel-in-progress: false + +jobs: + past-pytorch-docker: + name: "Past PyTorch Docker" + strategy: + fail-fast: false + matrix: + version: ["1.13", "1.12", "1.11"] + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Check out code + uses: actions/checkout@v4 + - + id: get-base-image + name: Get Base Image + env: + framework_version: ${{ matrix.version }} + run: | + echo "base_image=$(python3 -c 'import os; from utils.past_ci_versions import past_versions_testing; base_image = past_versions_testing["pytorch"][os.environ["framework_version"]]["base_image"]; print(base_image)')" >> $GITHUB_OUTPUT + - + name: Print Base Image + run: | + echo ${{ steps.get-base-image.outputs.base_image }} + - + name: Login to DockerHub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v3 + with: + context: ./docker/transformers-past-gpu + build-args: | + REF=main + BASE_DOCKER_IMAGE=${{ steps.get-base-image.outputs.base_image }} + FRAMEWORK=pytorch + VERSION=${{ matrix.version }} + push: true + tags: huggingface/transformers-pytorch-past-${{ matrix.version }}-gpu + + past-tensorflow-docker: + name: "Past TensorFlow Docker" + strategy: + fail-fast: false + matrix: + version: ["2.11", "2.10", "2.9", "2.8", "2.7", "2.6", "2.5"] + runs-on: + group: aws-general-8-plus + steps: + - + name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + - + name: Check out code + uses: actions/checkout@v4 + - + id: get-base-image + name: Get Base Image + env: + framework_version: ${{ matrix.version }} + run: | + echo "base_image=$(python3 -c 'import os; from utils.past_ci_versions import past_versions_testing; base_image = past_versions_testing["tensorflow"][os.environ["framework_version"]]["base_image"]; print(base_image)')" >> $GITHUB_OUTPUT + - + name: Print Base Image + run: | + echo ${{ steps.get-base-image.outputs.base_image }} + - + name: Login to DockerHub + uses: docker/login-action@v2 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_PASSWORD }} + - + name: Build and push + uses: docker/build-push-action@v3 + with: + context: ./docker/transformers-past-gpu + build-args: | + REF=main + BASE_DOCKER_IMAGE=${{ steps.get-base-image.outputs.base_image }} + FRAMEWORK=tensorflow + VERSION=${{ matrix.version }} + push: true + tags: huggingface/transformers-tensorflow-past-${{ matrix.version }}-gpu diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build_documentation.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..c55638ded1497c574639d8f352dbe1ed303a06fb --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build_documentation.yml @@ -0,0 +1,23 @@ +name: Build documentation + +on: + workflow_dispatch: + push: + branches: + - main + - doc-builder* + - v*-release + - use_templates + +jobs: + build: + uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main + with: + commit_sha: ${{ github.sha }} + package: transformers + notebook_folder: transformers_doc + languages: ar de en es fr hi it ko pt tr zh ja te + custom_container: huggingface/transformers-doc-builder + secrets: + token: ${{ secrets.HUGGINGFACE_PUSH }} + hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build_pr_documentation.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build_pr_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..f698f860b2f93c64a8b4d5c9dfeace0133c99eed --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/build_pr_documentation.yml @@ -0,0 +1,18 @@ +name: Build PR Documentation + +on: + pull_request: + +concurrency: + group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +jobs: + build: + uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main + with: + commit_sha: ${{ github.event.pull_request.head.sha }} + pr_number: ${{ github.event.number }} + package: transformers + languages: ar de en es fr hi it ko pt tr zh ja te + custom_container: huggingface/transformers-doc-builder diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/change_pr_to_draft.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/change_pr_to_draft.yml new file mode 100644 index 0000000000000000000000000000000000000000..f44a0cf1420feb94f29043504392a29254f077d3 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/change_pr_to_draft.yml @@ -0,0 +1,25 @@ +name: Change PR to draft + +on: + pull_request_target: + types: [opened, reopened] + +jobs: + convert_pr_to_draft: + runs-on: ubuntu-22.04 + name: Convert PR to draft + permissions: + pull-requests: write + contents: write + if: github.event.pull_request.draft == false + steps: + - name: Convert PR to draft + shell: bash + env: + PR_NUMBER: ${{ github.event.number }} + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + REPO: ${{ github.repository }} + run: | + echo $PR_NUMBER + gh pr ready $PR_NUMBER --repo $REPO --undo + gh pr comment $PR_NUMBER --repo $REPO --body "Hi 👋, thank you for opening this pull request! The pull request is converted to draft by default. When it is ready for review, please click the \`Ready for review\` button (at the bottom of the PR page)." diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/check_failed_model_tests.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/check_failed_model_tests.yml new file mode 100644 index 0000000000000000000000000000000000000000..5963523fd76c9d5476d034ffe698c87491dc71e1 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/check_failed_model_tests.yml @@ -0,0 +1,128 @@ +name: Process failed tests + +on: + workflow_call: + inputs: + docker: + required: true + type: string + start_sha: + required: true + type: string + + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + CUDA_VISIBLE_DEVICES: 0,1 + + +jobs: + run_models_gpu: + name: " " + runs-on: + group: aws-g4dn-2xlarge-cache + container: + image: ${{ inputs.docker }} + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - uses: actions/download-artifact@v4 + with: + name: ci_results_run_models_gpu + path: /transformers/ci_results_run_models_gpu + + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Get target commit + working-directory: /transformers/utils + run: | + echo "END_SHA=$(TOKEN=${{ secrets.ACCESS_REPO_INFO_TOKEN }} python3 -c 'import os; from get_previous_daily_ci import get_last_daily_ci_run_commit; commit=get_last_daily_ci_run_commit(token=os.environ["TOKEN"]); print(commit)')" >> $GITHUB_ENV + + - name: Checkout to `start_sha` + working-directory: /transformers + run: git fetch && git checkout ${{ inputs.start_sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Check failed tests + working-directory: /transformers + run: python3 utils/check_bad_commit.py --start_commit ${{ inputs.start_sha }} --end_commit ${{ env.END_SHA }} --file ci_results_run_models_gpu/new_model_failures.json --output_file new_model_failures_with_bad_commit.json + + - name: Show results + working-directory: /transformers + run: | + ls -l new_model_failures_with_bad_commit.json + cat new_model_failures_with_bad_commit.json + + - name: Checkout back + working-directory: /transformers + run: | + git checkout ${{ inputs.start_sha }} + + - name: Process report + shell: bash + working-directory: /transformers + env: + TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }} + run: | + python3 utils/process_bad_commit_report.py + + - name: Process report + shell: bash + working-directory: /transformers + env: + TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }} + run: | + { + echo 'REPORT_TEXT<> "$GITHUB_ENV" + + - name: Send processed report + if: ${{ !endsWith(env.REPORT_TEXT, '{}') }} + uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001 + with: + # Slack channel id, channel name, or user id to post message. + # See also: https://api.slack.com/methods/chat.postMessage#channels + channel-id: '#transformers-ci-feedback-tests' + # For posting a rich message using Block Kit + payload: | + { + "blocks": [ + { + "type": "section", + "text": { + "type": "mrkdwn", + "text": "${{ env.REPORT_TEXT }}" + } + } + ] + } + env: + SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/check_tiny_models.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/check_tiny_models.yml new file mode 100644 index 0000000000000000000000000000000000000000..a2b4846051a05460d62a86cdc45d59b1fa323487 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/check_tiny_models.yml @@ -0,0 +1,82 @@ +name: Check Tiny Models + +on: + push: + branches: + - check_tiny_models* + repository_dispatch: + schedule: + - cron: "0 2 * * *" + +env: + TOKEN: ${{ secrets.TRANSFORMERS_HUB_BOT_HF_TOKEN }} + +jobs: + check_tiny_models: + name: Check tiny models + runs-on: ubuntu-22.04 + steps: + - name: Checkout transformers + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - uses: actions/checkout@v4 + - name: Set up Python 3.8 + uses: actions/setup-python@v5 + with: + # Semantic version range syntax or exact version of a Python version + python-version: '3.8' + # Optional - x64 or x86 architecture, defaults to x64 + architecture: 'x64' + + - name: Install + run: | + sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng cmake + pip install --upgrade pip + python -m pip install -U .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm,video,tf-cpu] + pip install tensorflow_probability + python -m pip install -U 'natten<0.15.0' + + - name: Create all tiny models (locally) + run: | + python utils/create_dummy_models.py tiny_local_models --all --num_workers 2 + + - name: Local tiny model reports artifacts + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: tiny_local_model_creation_reports + path: tiny_local_models/reports + + # GitHub-hosted runners have 2-core CPUs + - name: Run pipeline tests against all new (local) tiny models + run: | + OMP_NUM_THREADS=1 TRANSFORMERS_TINY_MODEL_PATH=tiny_local_models python -m pytest --max-worker-restart=0 -n 2 --dist=loadfile -s -rA --make-reports=tests_pipelines tests/models -m is_pipeline_test -k "test_pipeline_" | tee tests_output.txt + + - name: Test suite reports artifacts + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: tiny_local_model_creation_reports + path: reports/tests_pipelines + + - name: Create + Upload tiny models for new model architecture(s) + run: | + python utils/update_tiny_models.py --num_workers 2 + + - name: Full report + run: cat tiny_models/reports/tiny_model_creation_report.json + + - name: Failure report + run: cat tiny_models/reports/simple_failed_report.txt + + - name: Summary report + run: cat tiny_models/reports/tiny_model_summary.json + + - name: New tiny model creation reports artifacts + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: tiny_model_creation_reports + path: tiny_models/reports diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/doctest_job.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/doctest_job.yml new file mode 100644 index 0000000000000000000000000000000000000000..eb62b797b8eb5575a33a6c0452563d809ce7fce7 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/doctest_job.yml @@ -0,0 +1,83 @@ +name: Doctest job + +on: + workflow_call: + inputs: + job_splits: + required: true + type: string + split_keys: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + RUN_SLOW: yes + OMP_NUM_THREADS: 16 + MKL_NUM_THREADS: 16 + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + +jobs: + run_doctests: + name: " " + strategy: + max-parallel: 8 # 8 jobs at a time + fail-fast: false + matrix: + split_keys: ${{ fromJson(inputs.split_keys) }} + runs-on: + group: aws-g4dn-2xlarge-cache + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .[flax] + + - name: GPU visibility + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + run: pip freeze + + - name: Get doctest files + working-directory: /transformers + run: | + echo "${{ toJson(fromJson(inputs.job_splits)[matrix.split_keys]) }}" > doc_tests.txt + cat doc_tests.txt + + - name: Set `split_keys` + shell: bash + run: | + echo "${{ matrix.split_keys }}" + split_keys=${{ matrix.split_keys }} + split_keys=${split_keys//'/'/'_'} + echo "split_keys" + echo "split_keys=$split_keys" >> $GITHUB_ENV + + - name: Run doctests + working-directory: /transformers + run: | + cat doc_tests.txt + python3 -m pytest -v --make-reports doc_tests_gpu_${{ env.split_keys }} --doctest-modules $(cat doc_tests.txt) -sv --doctest-continue-on-failure --doctest-glob="*.md" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/doc_tests_gpu_${{ env.split_keys }}/failures_short.txt + + - name: "Test suite reports artifacts: doc_tests_gpu_test_reports_${{ env.split_keys }}" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: doc_tests_gpu_test_reports_${{ env.split_keys }} + path: /transformers/reports/doc_tests_gpu_${{ env.split_keys }} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/doctests.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/doctests.yml new file mode 100644 index 0000000000000000000000000000000000000000..472b07684ed12ada716cc5f561665e5947c2f835 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/doctests.yml @@ -0,0 +1,89 @@ +name: Doctests + +on: + push: + branches: + - run_doctest* + repository_dispatch: + schedule: + - cron: "17 2 * * *" + +env: + NUM_SLICES: 3 + +jobs: + setup: + name: Setup + runs-on: + group: aws-g4dn-2xlarge-cache + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + job_splits: ${{ steps.set-matrix.outputs.job_splits }} + split_keys: ${{ steps.set-matrix.outputs.split_keys }} + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Check values for matrix + working-directory: /transformers + run: | + python3 utils/split_doctest_jobs.py + python3 utils/split_doctest_jobs.py --only_return_keys --num_splits ${{ env.NUM_SLICES }} + + - id: set-matrix + working-directory: /transformers + name: Set values for matrix + run: | + echo "job_splits=$(python3 utils/split_doctest_jobs.py)" >> $GITHUB_OUTPUT + echo "split_keys=$(python3 utils/split_doctest_jobs.py --only_return_keys --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT + + call_doctest_job: + name: "Call doctest jobs" + needs: setup + strategy: + max-parallel: 1 # 1 split at a time (in `doctest_job.yml`, we set `8` to run 8 jobs at the same time) + fail-fast: false + matrix: + split_keys: ${{ fromJson(needs.setup.outputs.split_keys) }} + uses: ./.github/workflows/doctest_job.yml + with: + job_splits: ${{ needs.setup.outputs.job_splits }} + split_keys: ${{ toJson(matrix.split_keys) }} + secrets: inherit + + send_results: + name: Send results to webhook + runs-on: ubuntu-22.04 + if: always() + needs: [call_doctest_job] + steps: + - uses: actions/checkout@v4 + - uses: actions/download-artifact@v4 + - name: Send message to Slack + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + # Use `CI_SLACK_CHANNEL_DUMMY_TESTS` when doing experimentation + SLACK_REPORT_CHANNEL: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY_DOCS }} + run: | + pip install slack_sdk + python utils/notification_service_doc_tests.py + + - name: "Upload results" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: doc_test_results + path: doc_test_results diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/model_jobs.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/model_jobs.yml new file mode 100644 index 0000000000000000000000000000000000000000..0997a1112ad7da0b66e1b37314deee4465686a5a --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/model_jobs.yml @@ -0,0 +1,138 @@ +name: model jobs + +on: + workflow_call: + inputs: + folder_slices: + required: true + type: string + machine_type: + required: true + type: string + slice_id: + required: true + type: number + runner: + required: true + type: string + docker: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + CUDA_VISIBLE_DEVICES: 0,1 + +jobs: + run_models_gpu: + name: " " + strategy: + max-parallel: 8 + fail-fast: false + matrix: + folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }} + runs-on: + group: '${{ inputs.machine_type }}' + container: + image: ${{ inputs.docker }} + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Echo input and matrix info + shell: bash + run: | + echo "${{ inputs.folder_slices }}" + echo "${{ matrix.folders }}" + echo "${{ toJson(fromJson(inputs.folder_slices)[inputs.slice_id]) }}" + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') }} + working-directory: /transformers + run: | + python3 -m pip install -U datasets + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') && contains(inputs.docker, '-pytorch-') }} + working-directory: /transformers + run: | + python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ inputs.machine_type }}" + + if [ "${{ inputs.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ inputs.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ inputs.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run all tests on GPU + working-directory: /transformers + run: python3 -m pytest -rsfE -v --make-reports=${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: Run test + shell: bash + run: | + mkdir -p /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports + echo "hello" > /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt + echo "${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports" + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/model_jobs_amd.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/model_jobs_amd.yml new file mode 100644 index 0000000000000000000000000000000000000000..c90181ec6f1b6df86a90f9215ff13e64e7a2f781 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/model_jobs_amd.yml @@ -0,0 +1,128 @@ +name: model jobs + +on: + workflow_call: + inputs: + folder_slices: + required: true + type: string + machine_type: + required: true + type: string + slice_id: + required: true + type: number + runner: + required: true + type: string + docker: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + CUDA_VISIBLE_DEVICES: 0,1 + +jobs: + run_models_gpu: + name: " " + strategy: + max-parallel: 1 # For now, not to parallelize. Can change later if it works well. + fail-fast: false + matrix: + folders: ${{ fromJson(inputs.folder_slices)[inputs.slice_id] }} + runs-on: ['${{ inputs.machine_type }}', self-hosted, amd-gpu, '${{ inputs.runner }}'] + container: + image: ${{ inputs.docker }} + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Echo input and matrix info + shell: bash + run: | + echo "${{ inputs.folder_slices }}" + echo "${{ matrix.folders }}" + echo "${{ toJson(fromJson(inputs.folder_slices)[inputs.slice_id]) }}" + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') }} + working-directory: /transformers + run: | + python3 -m pip install -U datasets + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') && contains(inputs.docker, '-pytorch-') }} + working-directory: /transformers + run: | + python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + + - name: ROCM-SMI + run: | + rocm-smi + + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all tests on GPU + working-directory: /transformers + run: python3 -m pytest -rsfE -v --make-reports=${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} -m "not not_device_test" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: Run test + shell: bash + run: | + mkdir -p /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports + echo "hello" > /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt + echo "${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports" + + - name: "Test suite reports artifacts: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ inputs.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ inputs.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/new_model_pr_merged_notification.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/new_model_pr_merged_notification.yml new file mode 100644 index 0000000000000000000000000000000000000000..6282528c0b74cb07c021e7d3420c1221f2b40b38 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/new_model_pr_merged_notification.yml @@ -0,0 +1,68 @@ +# Used to notify core maintainers about new model PR being merged +name: New model PR merged notification + +on: + push: + branches: + - main + paths: + - 'src/transformers/models/*/modeling_*' + +jobs: + notify_new_model: + name: Notify new model + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Check new model + shell: bash + run: | + python -m pip install gitpython + python -c 'from utils.pr_slow_ci_models import get_new_model; new_model = get_new_model(diff_with_last_commit=True); print(new_model)' | tee output.txt + echo "NEW_MODEL=$(tail -n 1 output.txt)" >> $GITHUB_ENV + echo "COMMIT_SHA=$(git log -1 --format=%H)" >> $GITHUB_ENV + + - name: print commit sha + if: ${{ env.NEW_MODEL != ''}} + shell: bash + run: | + echo "$COMMIT_SHA" + + - name: print new model + if: ${{ env.NEW_MODEL != ''}} + shell: bash + run: | + echo "$NEW_MODEL" + + - name: Notify + if: ${{ env.NEW_MODEL != ''}} + uses: slackapi/slack-github-action@6c661ce58804a1a20f6dc5fbee7f0381b469e001 + with: + # Slack channel id, channel name, or user id to post message. + # See also: https://api.slack.com/methods/chat.postMessage#channels + channel-id: transformers-new-model-notification + # For posting a rich message using Block Kit + payload: | + { + "blocks": [ + { + "type": "header", + "text": { + "type": "plain_text", + "text": "New model!", + "emoji": true + } + }, + { + "type": "section", + "text": { + "type": "mrkdwn", + "text": " GH_ArthurZucker, GH_lysandrejik, GH_ydshieh" + } + } + ] + } + env: + SLACK_BOT_TOKEN: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/push-important-models.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/push-important-models.yml new file mode 100644 index 0000000000000000000000000000000000000000..6a5f5acbce0d2aeb42ed8a304dddf8914e65d55e --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/push-important-models.yml @@ -0,0 +1,135 @@ +name: Slow tests on important models (on Push - A10) + +on: + push: + branches: [ main ] + +env: + OUTPUT_SLACK_CHANNEL_ID: "C06L2SGMEEA" + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. # This token is created under the bot `hf-transformers-bot`. + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + +jobs: + get_modified_models: + name: "Get all modified files" + runs-on: ubuntu-latest + outputs: + matrix: ${{ steps.set-matrix.outputs.matrix }} + steps: + - name: Check out code + uses: actions/checkout@v4 + + - name: Get changed files + id: changed-files + uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42 + with: + files: src/transformers/models/** + + - name: Run step if only the files listed above change + if: steps.changed-files.outputs.any_changed == 'true' + id: set-matrix + env: + ALL_CHANGED_FILES: ${{ steps.changed-files.outputs.all_changed_files }} + run: | + model_arrays=() + for file in $ALL_CHANGED_FILES; do + model_path="${file#*models/}" + model_path="models/${model_path%%/*}" + if grep -qFx "$model_path" utils/important_models.txt; then + # Append the file to the matrix string + model_arrays+=("$model_path") + fi + done + matrix_string=$(printf '"%s", ' "${model_arrays[@]}" | sed 's/, $//') + echo "matrix=[$matrix_string]" >> $GITHUB_OUTPUT + test_modified_files: + needs: get_modified_models + name: Slow & FA2 tests + runs-on: + group: aws-g5-4xlarge-cache + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + if: ${{ needs.get_modified_models.outputs.matrix != '[]' && needs.get_modified_models.outputs.matrix != '' && fromJson(needs.get_modified_models.outputs.matrix)[0] != null }} + strategy: + fail-fast: false + matrix: + model-name: ${{ fromJson(needs.get_modified_models.outputs.matrix) }} + + steps: + - name: Check out code + uses: actions/checkout@v4 + + - name: Install locally transformers & other libs + run: | + apt install sudo + sudo -H pip install --upgrade pip + sudo -H pip uninstall -y transformers + sudo -H pip install -U -e ".[testing]" + MAX_JOBS=4 pip install flash-attn --no-build-isolation + pip install bitsandbytes + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Show installed libraries and their versions + run: pip freeze + + - name: Run FA2 tests + id: run_fa2_tests + run: + pytest -rsfE -m "flash_attn_test" --make-reports=${{ matrix.model-name }}_fa2_tests/ tests/${{ matrix.model-name }}/test_modeling_* + + - name: "Test suite reports artifacts: ${{ matrix.model-name }}_fa2_tests" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ matrix.model-name }}_fa2_tests + path: /transformers/reports/${{ matrix.model-name }}_fa2_tests + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ env.OUTPUT_SLACK_CHANNEL_ID }} + title: 🤗 Results of the FA2 tests - ${{ matrix.model-name }} + status: ${{ steps.run_fa2_tests.conclusion}} + slack_token: ${{ secrets.CI_SLACK_BOT_TOKEN }} + + - name: Run integration tests + id: run_integration_tests + if: always() + run: + pytest -rsfE -k "IntegrationTest" --make-reports=tests_integration_${{ matrix.model-name }} tests/${{ matrix.model-name }}/test_modeling_* + + - name: "Test suite reports artifacts: tests_integration_${{ matrix.model-name }}" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: tests_integration_${{ matrix.model-name }} + path: /transformers/reports/tests_integration_${{ matrix.model-name }} + + - name: Post to Slack + if: always() + uses: huggingface/hf-workflows/.github/actions/post-slack@main + with: + slack_channel: ${{ env.OUTPUT_SLACK_CHANNEL_ID }} + title: 🤗 Results of the Integration tests - ${{ matrix.model-name }} + status: ${{ steps.run_integration_tests.conclusion}} + slack_token: ${{ secrets.CI_SLACK_BOT_TOKEN }} + + - name: Tailscale # In order to be able to SSH when a test fails + if: ${{ runner.debug == '1'}} + uses: huggingface/tailscale-action@v1 + with: + authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }} + slackChannel: ${{ secrets.SLACK_CIFEEDBACK_CHANNEL }} + slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + waitForSSH: true diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/release-conda.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/release-conda.yml new file mode 100644 index 0000000000000000000000000000000000000000..c0e28d7a510d7fae787b7b007ac4235360a5d666 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/release-conda.yml @@ -0,0 +1,47 @@ +name: Release - Conda + +on: + push: + tags: + - v* + branches: + - conda_* + +env: + ANACONDA_API_TOKEN: ${{ secrets.ANACONDA_API_TOKEN }} + +jobs: + build_and_package: + runs-on: ubuntu-22.04 + defaults: + run: + shell: bash -l {0} + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + auto-activate-base: false + python-version: 3.8 + activate-environment: "build-transformers" + channels: huggingface + + - name: Setup conda env + run: | + conda install -c defaults anaconda-client conda-build + + - name: Extract version + run: echo "TRANSFORMERS_VERSION=`python setup.py --version`" >> $GITHUB_ENV + + - name: Build conda packages + run: | + conda info + conda list + conda-build .github/conda + + - name: Upload to Anaconda + run: anaconda upload `conda-build .github/conda --output` --force diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-comment-ci.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-comment-ci.yml new file mode 100644 index 0000000000000000000000000000000000000000..a3df2ea97c24a9b5c58073e1d8d6ee854dd7fa97 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-comment-ci.yml @@ -0,0 +1,416 @@ +name: PR comment GitHub CI + +on: + issue_comment: + types: + - created + branches-ignore: + - main +concurrency: + group: ${{ github.workflow }}-${{ github.event.issue.number }}-${{ startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow') }} + cancel-in-progress: true +permissions: read-all + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + CUDA_VISIBLE_DEVICES: 0,1 + +jobs: + get-pr-number: + runs-on: ubuntu-22.04 + name: Get PR number + # For security: only allow team members to run + if: ${{ github.event.issue.state == 'open' && contains(fromJSON('["ydshieh", "ArthurZucker", "zucchini-nlp", "qubvel", "molbap", "gante", "LysandreJik", "Cyrilvallez", "Rocketknight1", "SunMarc", "muellerzr"]'), github.actor) && (startsWith(github.event.comment.body, 'run-slow') || startsWith(github.event.comment.body, 'run slow') || startsWith(github.event.comment.body, 'run_slow')) }} + outputs: + PR_NUMBER: ${{ steps.set_pr_number.outputs.PR_NUMBER }} + steps: + - name: Get PR number + shell: bash + run: | + if [[ "${{ github.event.issue.number }}" != "" && "${{ github.event.issue.pull_request }}" != "" ]]; then + echo "PR_NUMBER=${{ github.event.issue.number }}" >> $GITHUB_ENV + else + echo "PR_NUMBER=" >> $GITHUB_ENV + fi + + - name: Check PR number + shell: bash + run: | + echo "${{ env.PR_NUMBER }}" + + - name: Set PR number + id: set_pr_number + run: echo "PR_NUMBER=${{ env.PR_NUMBER }}" >> "$GITHUB_OUTPUT" + + get-sha: + runs-on: ubuntu-22.04 + needs: get-pr-number + if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}} + outputs: + PR_HEAD_SHA: ${{ steps.get_sha.outputs.PR_HEAD_SHA }} + PR_MERGE_SHA: ${{ steps.get_sha.outputs.PR_MERGE_SHA }} + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: "0" + ref: "refs/pull/${{needs.get-pr-number.outputs.PR_NUMBER}}/merge" + + - name: Get SHA (and verify timestamps against the issue comment date) + id: get_sha + env: + PR_NUMBER: ${{ needs.get-pr-number.outputs.PR_NUMBER }} + COMMENT_DATE: ${{ github.event.comment.created_at }} + run: | + git fetch origin refs/pull/$PR_NUMBER/head:refs/remotes/pull/$PR_NUMBER/head + git checkout refs/remotes/pull/$PR_NUMBER/head + echo "PR_HEAD_SHA: $(git log -1 --format=%H)" + echo "PR_HEAD_SHA=$(git log -1 --format=%H)" >> "$GITHUB_OUTPUT" + git fetch origin refs/pull/$PR_NUMBER/merge:refs/remotes/pull/$PR_NUMBER/merge + git checkout refs/remotes/pull/$PR_NUMBER/merge + echo "PR_MERGE_SHA: $(git log -1 --format=%H)" + echo "PR_MERGE_SHA=$(git log -1 --format=%H)" >> "$GITHUB_OUTPUT" + PR_MERGE_COMMIT_TIMESTAMP=$(git log -1 --date=unix --format=%cd) + echo "PR_MERGE_COMMIT_TIMESTAMP: $PR_MERGE_COMMIT_TIMESTAMP" + COMMENT_TIMESTAMP=$(date -d "${COMMENT_DATE}" +"%s") + echo "COMMENT_DATE: $COMMENT_DATE" + echo "COMMENT_TIMESTAMP: $COMMENT_TIMESTAMP" + if [ $COMMENT_TIMESTAMP -le $PR_MERGE_COMMIT_TIMESTAMP ]; then + echo "Last commit on the pull request is newer than the issue comment triggering this run! Abort!"; + exit -1; + fi + + # use a python script to handle this complex logic + # case 1: `run-slow` (auto. infer with limited number of models, but in particular, new model) + # case 2: `run-slow model_1, model_2` + get-tests: + runs-on: ubuntu-22.04 + needs: [get-pr-number, get-sha] + if: ${{ needs.get-pr-number.outputs.PR_NUMBER != ''}} + outputs: + models: ${{ steps.models_to_run.outputs.models }} + quantizations: ${{ steps.models_to_run.outputs.quantizations }} + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: "0" + ref: "refs/pull/${{needs.get-pr-number.outputs.PR_NUMBER}}/merge" + + - name: Verify merge commit SHA + env: + VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }} + run: | + PR_MERGE_SHA=$(git log -1 --format=%H) + if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then + echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!"; + exit -1; + fi + + - name: Get models to test + env: + PR_COMMENT: ${{ github.event.comment.body }} + run: | + python -m pip install GitPython + python utils/pr_slow_ci_models.py --message "$PR_COMMENT" | tee output.txt + echo "models=$(tail -n 1 output.txt)" >> $GITHUB_ENV + python utils/pr_slow_ci_models.py --message "$PR_COMMENT" --quantization | tee output2.txt + echo "quantizations=$(tail -n 1 output2.txt)" >> $GITHUB_ENV + + - name: Show models to test + id: models_to_run + run: | + echo "${{ env.models }}" + echo "models=${{ env.models }}" >> $GITHUB_ENV + echo "models=${{ env.models }}" >> $GITHUB_OUTPUT + echo "${{ env.quantizations }}" + echo "quantizations=${{ env.quantizations }}" >> $GITHUB_OUTPUT + + reply_to_comment: + name: Reply to the comment + if: ${{ needs.get-tests.outputs.models != '[]' || needs.get-tests.outputs.quantizations != '[]' }} + needs: [get-pr-number, get-tests] + permissions: + pull-requests: write + runs-on: ubuntu-22.04 + steps: + - name: Reply to the comment + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + MODELS: ${{ needs.get-tests.outputs.models }} + BODY: "This comment contains run-slow, running the specified jobs:\n\nmodels: ${{ needs.get-tests.outputs.models }}\nquantizations: ${{ needs.get-tests.outputs.quantizations }}" + run: | + gh api \ + --method POST \ + -H "Accept: application/vnd.github+json" \ + -H "X-GitHub-Api-Version: 2022-11-28" \ + repos/${{ github.repository }}/issues/${{ needs.get-pr-number.outputs.PR_NUMBER }}/comments \ + -f "body=This comment contains run-slow, running the specified jobs: ${{ env.BODY }} ..." + + create_run: + name: Create run + if: ${{ needs.get-tests.outputs.models != '[]' || needs.get-tests.outputs.quantizations != '[]' }} + needs: [get-sha, get-tests, reply_to_comment] + permissions: + statuses: write + runs-on: ubuntu-22.04 + steps: + - name: Create Run + id: create_run + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Create a commit status (pending) for a run of this workflow. The status has to be updated later in `update_run_status`. + # See https://docs.github.com/en/rest/commits/statuses?apiVersion=2022-11-28#create-a-commit-status + GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }} + run: | + gh api \ + --method POST \ + -H "Accept: application/vnd.github+json" \ + -H "X-GitHub-Api-Version: 2022-11-28" \ + repos/${{ github.repository }}/statuses/${{ needs.get-sha.outputs.PR_HEAD_SHA }} \ + -f "target_url=$GITHUB_RUN_URL" -f "state=pending" -f "description=Slow CI job" -f "context=pytest/custom-tests" + + run_models_gpu: + name: Run all tests for the model + if: ${{ needs.get-tests.outputs.models != '[]' }} + needs: [get-pr-number, get-sha, get-tests, create_run] + strategy: + fail-fast: false + matrix: + folders: ${{ fromJson(needs.get-tests.outputs.models) }} + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Echo input and matrix info + shell: bash + run: | + echo "${{ matrix.folders }}" + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: Checkout to PR merge commit + working-directory: /transformers + run: | + git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge + git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge + git log -1 --format=%H + + - name: Verify merge commit SHA + env: + VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }} + working-directory: /transformers + run: | + PR_MERGE_SHA=$(git log -1 --format=%H) + if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then + echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!"; + exit -1; + fi + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all tests on GPU + working-directory: /transformers + run: | + export CUDA_VISIBLE_DEVICES="$(python3 utils/set_cuda_devices_for_ci.py --test_folder ${{ matrix.folders }})" + echo $CUDA_VISIBLE_DEVICES + python3 -m pytest -v -rsfE --make-reports=${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: Make sure report directory exists + shell: bash + run: | + mkdir -p /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports + echo "hello" > /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/hello.txt + echo "${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports" + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports + + run_quantization_torch_gpu: + name: Run all tests for a quantization + if: ${{ needs.get-tests.outputs.quantizations != '[]' }} + needs: [get-pr-number, get-sha, get-tests, create_run] + strategy: + fail-fast: false + matrix: + folders: ${{ fromJson(needs.get-tests.outputs.quantizations) }} + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-quantization-latest-gpu + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Echo folder ${{ matrix.folders }} + shell: bash + run: | + echo "${{ matrix.folders }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'quantization/'/'quantization_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: Checkout to PR merge commit + working-directory: /transformers + run: | + git fetch origin refs/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge:refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge + git checkout refs/remotes/pull/${{ needs.get-pr-number.outputs.PR_NUMBER }}/merge + git log -1 --format=%H + + - name: Verify merge commit SHA + env: + VERIFIED_PR_MERGE_SHA: ${{ needs.get-sha.outputs.PR_MERGE_SHA }} + working-directory: /transformers + run: | + PR_MERGE_SHA=$(git log -1 --format=%H) + if [ $PR_MERGE_SHA != $VERIFIED_PR_MERGE_SHA ]; then + echo "The merged commit SHA is not the same as the verified one! Security issue detected, abort the workflow!"; + exit -1; + fi + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run quantization tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: Make sure report directory exists + shell: bash + run: | + mkdir -p /transformers/reports/${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports + echo "hello" > /transformers/reports/${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports/hello.txt + echo "${{ env.machine_type }}_run_quantization_gpu_${{ matrix.folders }}_test_reports" + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports + + update_run_status: + name: Update Check Run Status + needs: [get-sha, create_run, run_models_gpu, run_quantization_torch_gpu] + permissions: + statuses: write + if: ${{ always() && needs.create_run.result == 'success' }} + runs-on: ubuntu-22.04 + env: + GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GITHUB_RUN_URL: https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }} + STATUS_OK: ${{ contains(fromJSON('["skipped", "success"]'), needs.run_models_gpu.result) && contains(fromJSON('["skipped", "success"]'), needs.run_quantization_torch_gpu.result) }} + steps: + - name: Get `run_models_gpu` job status + run: | + echo "${{ needs.run_models_gpu.result }}" + echo "${{ needs.run_quantization_torch_gpu.result }}" + echo $STATUS_OK + if [ "$STATUS_OK" = "true" ]; then + echo "STATUS=success" >> $GITHUB_ENV + else + echo "STATUS=failure" >> $GITHUB_ENV + fi + + - name: Update PR commit statuses + run: | + echo "${{ needs.run_models_gpu.result }}" + echo "${{ env.STATUS }}" + gh api \ + --method POST \ + -H "Accept: application/vnd.github+json" \ + -H "X-GitHub-Api-Version: 2022-11-28" \ + repos/${{ github.repository }}/statuses/${{ needs.get-sha.outputs.PR_HEAD_SHA }} \ + -f "target_url=$GITHUB_RUN_URL" -f "state=${{ env.STATUS }}" -f "description=Slow CI job" -f "context=pytest/custom-tests" diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-nightly-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-nightly-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..5538e2d56e749091d0b7fdccdf24ed95b39e5a3d --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-nightly-caller.yml @@ -0,0 +1,43 @@ +name: Self-hosted runner (nightly-ci) + + +on: + repository_dispatch: + schedule: + - cron: "17 2 * * *" + push: + branches: + - run_nightly_ci* + +jobs: + build_nightly_ci_images: + name: Build Nightly CI Docker Images + if: (github.event_name == 'schedule') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_nightly_ci')) + uses: ./.github/workflows/build-nightly-ci-docker-images.yml + secrets: inherit + + model-ci: + name: Model CI + needs: [build_nightly_ci_images] + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-past-future" + runner: ci + docker: huggingface/transformers-all-latest-torch-nightly-gpu + ci_event: Nightly CI + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + needs: [build_nightly_ci_images] + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-past-future" + runner: ci + # test deepspeed nightly build with the latest release torch + docker: huggingface/transformers-pytorch-deepspeed-latest-gpu + ci_event: Nightly CI + working-directory-prefix: /workspace + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-nightly-past-ci-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-nightly-past-ci-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..46d811d4a43394a86f256669b69d19cf5d109fab --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-nightly-past-ci-caller.yml @@ -0,0 +1,99 @@ +name: Self-hosted runner (nightly-past-ci-caller) + +on: + schedule: + - cron: "17 2,14 * * *" + push: + branches: + - run_past_ci* + +jobs: + get_number: + name: Get number + runs-on: ubuntu-22.04 + outputs: + run_number: ${{ steps.get_number.outputs.run_number }} + steps: + - name: Get number + id: get_number + run: | + echo "${{ github.run_number }}" + echo "$(python3 -c 'print(int(${{ github.run_number }}) % 10)')" + echo "run_number=$(python3 -c 'print(int(${{ github.run_number }}) % 10)')" >> $GITHUB_OUTPUT + + run_past_ci_tensorflow_2-11: + name: TensorFlow 2.11 + needs: get_number + if: needs.get_number.outputs.run_number == 3 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.11" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-10: + name: TensorFlow 2.10 + needs: get_number + if: needs.get_number.outputs.run_number == 4 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.10" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-9: + name: TensorFlow 2.9 + needs: get_number + if: needs.get_number.outputs.run_number == 5 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.9" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-8: + name: TensorFlow 2.8 + needs: get_number + if: needs.get_number.outputs.run_number == 6 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.8" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-7: + name: TensorFlow 2.7 + needs: get_number + if: needs.get_number.outputs.run_number == 7 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.7" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-6: + name: TensorFlow 2.6 + needs: get_number + if: needs.get_number.outputs.run_number == 8 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.6" + sha: ${{ github.sha }} + secrets: inherit + + run_past_ci_tensorflow_2-5: + name: TensorFlow 2.5 + needs: get_number + if: needs.get_number.outputs.run_number == 9 && (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')) + uses: ./.github/workflows/self-past-caller.yml + with: + framework: tensorflow + version: "2.5" + sha: ${{ github.sha }} + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-past-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-past-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..1929a01c34d94726d1e820924933cec1412a02d6 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-past-caller.yml @@ -0,0 +1,40 @@ +name: Self-hosted runner (past-ci) + + +on: + workflow_call: + inputs: + framework: + required: true + type: string + version: + required: true + type: string + # Use this to control the commit to test against + sha: + default: 'main' + required: false + type: string + +jobs: + model-ci: + name: Model CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-past-future" + runner: past-ci + docker: huggingface/transformers-${{ inputs.framework }}-past-${{ inputs.version }}-gpu + ci_event: Past CI - ${{ inputs.framework }}-${{ inputs.version }} + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-past-future" + runner: past-ci + docker: huggingface/transformers-${{ inputs.framework }}-past-${{ inputs.version }}-gpu + ci_event: Past CI - ${{ inputs.framework }}-${{ inputs.version }} + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi210-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi210-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..45b325f7b357bf838f2f876bb7f4748ed6204ee2 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi210-caller.yml @@ -0,0 +1,25 @@ +name: Self-hosted runner (AMD mi210 CI caller) + +on: + #workflow_run: + # workflows: ["Self-hosted runner (push-caller)"] + # branches: ["main"] + # types: [completed] + push: + branches: + - run_amd_push_ci_caller* + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + +jobs: + run_amd_ci: + name: AMD mi210 + if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_amd_push_ci_caller'))) + uses: ./.github/workflows/self-push-amd.yml + with: + gpu_flavor: mi210 + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi250-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi250-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..91b978b593d0b5b37d2415677a35c8f73c434dac --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi250-caller.yml @@ -0,0 +1,25 @@ +name: Self-hosted runner (AMD mi250 CI caller) + +on: + #workflow_run: + # workflows: ["Self-hosted runner (push-caller)"] + # branches: ["main"] + # types: [completed] + push: + branches: + - run_amd_push_ci_caller* + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + +jobs: + run_amd_ci: + name: AMD mi250 + if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_amd_push_ci_caller'))) + uses: ./.github/workflows/self-push-amd.yml + with: + gpu_flavor: mi250 + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi300-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi300-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..797916125a24fbaf75ae74e87eddbd46a7507154 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd-mi300-caller.yml @@ -0,0 +1,25 @@ +name: Self-hosted runner (AMD mi300 CI caller) + +on: + #workflow_run: + # workflows: ["Self-hosted runner (push-caller)"] + # branches: ["main"] + # types: [completed] + push: + branches: + - run_amd_push_ci_caller* + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + +jobs: + run_amd_ci: + name: AMD mi300 + if: (cancelled() != true) && ((github.event_name == 'workflow_run') || ((github.event_name == 'push') && (startsWith(github.ref_name, 'run_amd_push_ci_caller') || startsWith(github.ref_name, 'mi300-ci')))) + uses: ./.github/workflows/self-push-amd.yml + with: + gpu_flavor: mi300 + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd.yml new file mode 100644 index 0000000000000000000000000000000000000000..621061988949d3f57aa47b2a3740603c86800733 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-amd.yml @@ -0,0 +1,334 @@ +name: Self-hosted runner AMD GPU (push) + +on: + workflow_call: + inputs: + gpu_flavor: + required: true + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + PYTEST_TIMEOUT: 60 + TF_FORCE_GPU_ALLOW_GROWTH: true + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + +jobs: + check_runner_status: + name: Check Runner Status + runs-on: ubuntu-22.04 + steps: + - name: Checkout transformers + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - name: Check Runner Status + run: python utils/check_self_hosted_runner.py --target_runners amd-mi210-single-gpu-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + + check_runners: + name: Check Runners + needs: check_runner_status + strategy: + matrix: + machine_type: [single-gpu, multi-gpu] + runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}'] + container: + image: huggingface/transformers-pytorch-amd-gpu-push-ci # <--- We test only for PyTorch for now + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: ROCM-SMI + run: | + rocm-smi + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + setup_gpu: + name: Setup + needs: check_runners + strategy: + matrix: + machine_type: [single-gpu, multi-gpu] + runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}'] + container: + image: huggingface/transformers-pytorch-amd-gpu-push-ci # <--- We test only for PyTorch for now + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + matrix: ${{ steps.set-matrix.outputs.matrix }} + test_map: ${{ steps.set-matrix.outputs.test_map }} + env: + # `CI_BRANCH_PUSH`: The branch name from the push event + # `CI_BRANCH_WORKFLOW_RUN`: The name of the branch on which this workflow is triggered by `workflow_run` event + # `CI_SHA_PUSH`: The commit SHA from the push event + # `CI_SHA_WORKFLOW_RUN`: The commit SHA that triggers this workflow by `workflow_run` event + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # `CI_BRANCH`: The non-empty branch name from the above two (one and only one of them is empty) + # `CI_SHA`: The non-empty commit SHA from the above two (one and only one of them is empty) + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Fetch the tests to run + working-directory: /transformers + # TODO: add `git-python` in the docker images + run: | + pip install --upgrade git-python + python3 utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt + + - name: Report fetched tests + uses: actions/upload-artifact@v4 + with: + name: test_fetched + path: /transformers/test_preparation.txt + + - id: set-matrix + name: Organize tests into models + working-directory: /transformers + # The `keys` is used as GitHub actions matrix for jobs, i.e. `models/bert`, `tokenization`, `pipeline`, etc. + # The `test_map` is used to get the actual identified test files under each key. + # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail) + run: | + if [ -f test_map.json ]; then + keys=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(d)') + test_map=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); print(test_map)') + else + keys=$(python3 -c 'keys = ["dummy"]; print(keys)') + test_map=$(python3 -c 'test_map = {"dummy": []}; print(test_map)') + fi + echo $keys + echo $test_map + echo "matrix=$keys" >> $GITHUB_OUTPUT + echo "test_map=$test_map" >> $GITHUB_OUTPUT + + run_models_gpu: + name: Model tests + needs: setup_gpu + # `dummy` means there is no test to run + if: contains(fromJson(needs.setup_gpu.outputs.matrix), 'dummy') != true + strategy: + fail-fast: false + matrix: + folders: ${{ fromJson(needs.setup_gpu.outputs.matrix) }} + machine_type: [single-gpu, multi-gpu] + runs-on: [self-hosted, amd-gpu, '${{ matrix.machine_type }}', '${{ inputs.gpu_flavor }}'] + container: + image: huggingface/transformers-pytorch-amd-gpu-push-ci # <--- We test only for PyTorch for now + options: --device /dev/kfd --device /dev/dri --env ROCR_VISIBLE_DEVICES --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + echo "${{ fromJson(needs.setup_gpu.outputs.test_map)[matrix.folders] }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: ROCM-SMI + run: | + rocm-smi + - name: ROCM-INFO + run: | + rocminfo | grep "Agent" -A 14 + - name: Show ROCR environment + run: | + echo "ROCR: $ROCR_VISIBLE_DEVICES" + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports ${{ fromJson(needs.setup_gpu.outputs.test_map)[matrix.folders] }} -m "not not_device_test" + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ matrix.machine_type }}_run_models_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ matrix.machine_type }}_run_models_gpu_${{ matrix.folders }}_test_reports + + send_results: + name: Send results to webhook + runs-on: ubuntu-22.04 + if: always() + needs: [ + check_runner_status, + check_runners, + setup_gpu, + run_models_gpu, +# run_tests_torch_cuda_extensions_single_gpu, +# run_tests_torch_cuda_extensions_multi_gpu + ] + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + - name: Preliminary job status + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + echo "Runner availability: ${{ needs.check_runner_status.result }}" + echo "Setup status: ${{ needs.setup_gpu.result }}" + echo "Runner status: ${{ needs.check_runners.result }}" + + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - uses: actions/checkout@v4 + # To avoid failure when multiple commits are merged into `main` in a short period of time. + # Checking out to an old commit beyond the fetch depth will get an error `fatal: reference is not a tree: ... + # (Only required for `workflow_run` event, where we get the latest HEAD on `main` instead of the event commit) + with: + fetch-depth: 20 + + - name: Update clone using environment variables + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - uses: actions/download-artifact@v4 + - name: Send message to Slack + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} + CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} + CI_SLACK_CHANNEL_ID_AMD: ${{ secrets.CI_SLACK_CHANNEL_ID_AMD }} + CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }} + CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_AMD }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + CI_EVENT: Push CI (AMD) - ${{ inputs.gpu_flavor }} + CI_TITLE_PUSH: ${{ github.event.head_commit.message }} + CI_TITLE_WORKFLOW_RUN: ${{ github.event.workflow_run.head_commit.message }} + CI_SHA: ${{ env.CI_SHA }} + RUNNER_STATUS: ${{ needs.check_runner_status.result }} + RUNNER_ENV_STATUS: ${{ needs.check_runners.result }} + SETUP_STATUS: ${{ needs.setup_gpu.result }} + + # We pass `needs.setup_gpu.outputs.matrix` as the argument. A processing in `notification_service.py` to change + # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`. + run: | + pip install huggingface_hub + pip install slack_sdk + pip show slack_sdk + python utils/notification_service.py "${{ needs.setup_gpu.outputs.matrix }}" diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..59adde4c54e077b4e944f8f6ae578cb6de05f8b4 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push-caller.yml @@ -0,0 +1,54 @@ +# Used to trigger self-push CI +name: Self-hosted runner (push-caller) + +on: + push: + branches: + - main + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + +jobs: + check-for-setup: + runs-on: ubuntu-22.04 + name: Check if setup was changed + outputs: + changed: ${{ steps.was_changed.outputs.changed }} + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: "2" + + - name: Get changed files + id: changed-files + uses: tj-actions/changed-files@v41 + + - name: Was setup changed + id: was_changed + run: | + for file in ${{ steps.changed-files.outputs.all_changed_files }}; do + if [ `basename "${file}"` = "setup.py" ]; then + echo "changed=1" >> $GITHUB_OUTPUT + fi + done + + build-docker-containers: + needs: check-for-setup + if: (github.event_name == 'push') && (needs.check-for-setup.outputs.changed == '1') + uses: ./.github/workflows/build-docker-images.yml + with: + image_postfix: "-push-ci" + secrets: inherit + + run_push_ci: + name: Trigger Push CI + runs-on: ubuntu-22.04 + if: ${{ always() }} + needs: build-docker-containers + steps: + - name: Trigger push CI via workflow_run + run: echo "Trigger push CI via workflow_run" \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push.yml new file mode 100644 index 0000000000000000000000000000000000000000..3b3be41e3e9b561b0b038ae4ac74c74d9ca5d4ce --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-push.yml @@ -0,0 +1,652 @@ +name: Self-hosted runner (push) + +on: + workflow_run: + workflows: ["Self-hosted runner (push-caller)"] + branches: ["main"] + types: [completed] + push: + branches: + - ci_* + - ci-* + paths: + - "src/**" + - "tests/**" + - ".github/**" + - "templates/**" + - "utils/**" + repository_dispatch: + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + PYTEST_TIMEOUT: 60 + TF_FORCE_GPU_ALLOW_GROWTH: true + CUDA_VISIBLE_DEVICES: 0,1 + +jobs: + setup: + name: Setup + strategy: + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu-push-ci + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + matrix: ${{ steps.set-matrix.outputs.matrix }} + test_map: ${{ steps.set-matrix.outputs.test_map }} + env: + # `CI_BRANCH_PUSH`: The branch name from the push event + # `CI_BRANCH_WORKFLOW_RUN`: The name of the branch on which this workflow is triggered by `workflow_run` event + # `CI_SHA_PUSH`: The commit SHA from the push event + # `CI_SHA_WORKFLOW_RUN`: The commit SHA that triggers this workflow by `workflow_run` event + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # `CI_BRANCH`: The non-empty branch name from the above two (one and only one of them is empty) + # `CI_SHA`: The non-empty commit SHA from the above two (one and only one of them is empty) + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Fetch the tests to run + working-directory: /transformers + # TODO: add `git-python` in the docker images + run: | + pip install --upgrade git-python + python3 utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt + + - name: Report fetched tests + uses: actions/upload-artifact@v4 + with: + name: test_fetched + path: /transformers/test_preparation.txt + + - id: set-matrix + name: Organize tests into models + working-directory: /transformers + # The `keys` is used as GitHub actions matrix for jobs, i.e. `models/bert`, `tokenization`, `pipeline`, etc. + # The `test_map` is used to get the actual identified test files under each key. + # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail) + run: | + if [ -f test_map.json ]; then + keys=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); d = list(test_map.keys()); print(d)') + test_map=$(python3 -c 'import json; fp = open("test_map.json"); test_map = json.load(fp); fp.close(); print(test_map)') + else + keys=$(python3 -c 'keys = ["dummy"]; print(keys)') + test_map=$(python3 -c 'test_map = {"dummy": []}; print(test_map)') + fi + echo $keys + echo $test_map + echo "matrix=$keys" >> $GITHUB_OUTPUT + echo "test_map=$test_map" >> $GITHUB_OUTPUT + + run_tests_single_gpu: + name: Model tests + needs: setup + # `dummy` means there is no test to run + if: contains(fromJson(needs.setup.outputs.matrix), 'dummy') != true + strategy: + fail-fast: false + matrix: + folders: ${{ fromJson(needs.setup.outputs.matrix) }} + machine_type: [aws-g4dn-2xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu-push-ci + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + echo "${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} + + run_tests_multi_gpu: + name: Model tests + needs: setup + # `dummy` means there is no test to run + if: contains(fromJson(needs.setup.outputs.matrix), 'dummy') != true + strategy: + fail-fast: false + matrix: + folders: ${{ fromJson(needs.setup.outputs.matrix) }} + machine_type: [aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu-push-ci + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Update clone using environment variables + working-directory: /transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Echo folder ${{ matrix.folders }} + shell: bash + # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to + # set the artifact folder names (because the character `/` is not allowed). + run: | + echo "${{ matrix.folders }}" + echo "${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'models/'/'models_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + env: + MKL_SERVICE_FORCE_INTEL: 1 + working-directory: /transformers + run: | + python3 -m pytest -n 2 --dist=loadfile -v --make-reports=${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} ${{ fromJson(needs.setup.outputs.test_map)[matrix.folders] }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_tests_gpu_${{ matrix.folders }} + + run_tests_torch_cuda_extensions_single_gpu: + name: Torch CUDA extension tests + needs: setup + if: contains(fromJson(needs.setup.outputs.matrix), 'deepspeed') || contains(fromJson(needs.setup.outputs.matrix), 'extended') + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Set `machine_type` for report and artifact names + working-directory: /workspace/transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Update clone using environment variables + working-directory: /workspace/transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /workspace/transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Remove cached torch extensions + run: rm -rf /github/home/.cache/torch_extensions/ + + # To avoid unknown test failures + - name: Pre build DeepSpeed *again* + working-directory: /workspace + run: | + python3 -m pip uninstall -y deepspeed + DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /workspace/transformers + run: | + python utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /workspace/transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + working-directory: /workspace/transformers + # TODO: Here we pass all tests in the 2 folders for simplicity. It's better to pass only the identified tests. + run: | + python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + path: /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + + run_tests_torch_cuda_extensions_multi_gpu: + name: Torch CUDA extension tests + needs: setup + if: contains(fromJson(needs.setup.outputs.matrix), 'deepspeed') || contains(fromJson(needs.setup.outputs.matrix), 'extended') + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - name: Set `machine_type` for report and artifact names + working-directory: /workspace/transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Update clone using environment variables + working-directory: /workspace/transformers + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /workspace/transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Remove cached torch extensions + run: rm -rf /github/home/.cache/torch_extensions/ + + # To avoid unknown test failures + - name: Pre build DeepSpeed *again* + working-directory: /workspace + run: | + python3 -m pip uninstall -y deepspeed + DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /workspace/transformers + run: | + python utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /workspace/transformers + run: pip freeze + + - name: Run all non-slow selected tests on GPU + working-directory: /workspace/transformers + # TODO: Here we pass all tests in the 2 folders for simplicity. It's better to pass only the identified tests. + run: | + python -m pytest -n 1 --dist=loadfile -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + path: /workspace/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + + send_results: + name: Send results to webhook + runs-on: ubuntu-22.04 + if: always() + needs: [ + setup, + run_tests_single_gpu, + run_tests_multi_gpu, + run_tests_torch_cuda_extensions_single_gpu, + run_tests_torch_cuda_extensions_multi_gpu + ] + env: + # For the meaning of these environment variables, see the job `Setup` + CI_BRANCH_PUSH: ${{ github.event.ref }} + CI_BRANCH_WORKFLOW_RUN: ${{ github.event.workflow_run.head_branch }} + CI_SHA_PUSH: ${{ github.event.head_commit.id }} + CI_SHA_WORKFLOW_RUN: ${{ github.event.workflow_run.head_sha }} + steps: + - name: Preliminary job status + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + echo "Setup status: ${{ needs.setup.result }}" + + # Necessary to get the correct branch name and commit SHA for `workflow_run` event + # We also take into account the `push` event (we might want to test some changes in a branch) + - name: Prepare custom environment variables + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + CI_BRANCH_PUSH=${CI_BRANCH_PUSH/'refs/heads/'/''} + echo $CI_BRANCH_PUSH + echo $CI_BRANCH_WORKFLOW_RUN + echo $CI_SHA_PUSH + echo $CI_SHA_WORKFLOW_RUN + [[ ! -z "$CI_BRANCH_PUSH" ]] && echo "CI_BRANCH=$CI_BRANCH_PUSH" >> $GITHUB_ENV || echo "CI_BRANCH=$CI_BRANCH_WORKFLOW_RUN" >> $GITHUB_ENV + [[ ! -z "$CI_SHA_PUSH" ]] && echo "CI_SHA=$CI_SHA_PUSH" >> $GITHUB_ENV || echo "CI_SHA=$CI_SHA_WORKFLOW_RUN" >> $GITHUB_ENV + + - name: print environment variables + run: | + echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}" + echo "env.CI_SHA = ${{ env.CI_SHA }}" + + - uses: actions/checkout@v4 + # To avoid failure when multiple commits are merged into `main` in a short period of time. + # Checking out to an old commit beyond the fetch depth will get an error `fatal: reference is not a tree: ... + # (Only required for `workflow_run` event, where we get the latest HEAD on `main` instead of the event commit) + with: + fetch-depth: 20 + + - name: Update clone using environment variables + run: | + echo "original branch = $(git branch --show-current)" + git fetch && git checkout ${{ env.CI_BRANCH }} + echo "updated branch = $(git branch --show-current)" + git checkout ${{ env.CI_SHA }} + echo "log = $(git log -n 1)" + + - uses: actions/download-artifact@v4 + - name: Send message to Slack + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} + CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} + CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }} + CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + CI_EVENT: push + CI_TITLE_PUSH: ${{ github.event.head_commit.message }} + CI_TITLE_WORKFLOW_RUN: ${{ github.event.workflow_run.head_commit.message }} + CI_SHA: ${{ env.CI_SHA }} + SETUP_STATUS: ${{ needs.setup.result }} + + # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change + # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`. + run: | + pip install huggingface_hub + pip install slack_sdk + pip show slack_sdk + python utils/notification_service.py "${{ needs.setup.outputs.matrix }}" diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..dc5c7b7e905bd88424452ab95da23af096bffa26 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-caller.yml @@ -0,0 +1,14 @@ +name: Self-hosted runner (AMD scheduled CI caller) + +on: + schedule: + - cron: "17 2 * * *" + +jobs: + run_scheduled_amd_ci: + name: Trigger Scheduled AMD CI + runs-on: ubuntu-22.04 + if: ${{ always() }} + steps: + - name: Trigger scheduled AMD CI via workflow_run + run: echo "Trigger scheduled AMD CI via workflow_run" diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-mi210-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-mi210-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..6109faca00932e9a2fdb11c1098aba1d7f7a8a5b --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-mi210-caller.yml @@ -0,0 +1,55 @@ +name: Self-hosted runner (AMD mi210 scheduled CI caller) + +on: + workflow_run: + workflows: ["Self-hosted runner (AMD scheduled CI caller)"] + branches: ["main"] + types: [completed] + push: + branches: + - run_amd_scheduled_ci_caller* + +jobs: + model-ci: + name: Model CI + uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi210 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi210 + secrets: inherit + + torch-pipeline: + name: Torch pipeline CI + uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main + with: + job: run_pipelines_torch_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi210 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi210 + secrets: inherit + + example-ci: + name: Example CI + uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main + with: + job: run_examples_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi210 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi210 + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-daily-amd" + runner: mi210 + docker: huggingface/transformers-pytorch-deepspeed-amd-gpu + ci_event: Scheduled CI (AMD) - mi210 + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-mi250-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-mi250-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..4c6284a78cda8eceda05db5c99f8136e47685883 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-amd-mi250-caller.yml @@ -0,0 +1,55 @@ +name: Self-hosted runner (AMD mi250 scheduled CI caller) + +on: + workflow_run: + workflows: ["Self-hosted runner (AMD scheduled CI caller)"] + branches: ["main"] + types: [completed] + push: + branches: + - run_amd_scheduled_ci_caller* + +jobs: + model-ci: + name: Model CI + uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main + with: + job: run_models_gpu + slack_report_channel: "#amd-hf-ci" + runner: mi250 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi250 + secrets: inherit + + torch-pipeline: + name: Torch pipeline CI + uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main + with: + job: run_pipelines_torch_gpu + slack_report_channel: "#amd-hf-ci" + runner: mi250 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi250 + secrets: inherit + + example-ci: + name: Example CI + uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main + with: + job: run_examples_gpu + slack_report_channel: "#amd-hf-ci" + runner: mi250 + docker: huggingface/transformers-pytorch-amd-gpu + ci_event: Scheduled CI (AMD) - mi250 + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + uses: huggingface/hf-workflows/.github/workflows/transformers_amd_ci_scheduled.yaml@main + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#amd-hf-ci" + runner: mi250 + docker: huggingface/transformers-pytorch-deepspeed-amd-gpu + ci_event: Scheduled CI (AMD) - mi250 + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-caller.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-caller.yml new file mode 100644 index 0000000000000000000000000000000000000000..75ea3bb24bc7fa2acda33e001780bafc26bc5f0b --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled-caller.yml @@ -0,0 +1,78 @@ +name: Self-hosted runner (scheduled) + + +on: + repository_dispatch: + schedule: + - cron: "17 2 * * *" + push: + branches: + - run_scheduled_ci* + +jobs: + model-ci: + name: Model CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_models_gpu + slack_report_channel: "#transformers-ci-daily-models" + runner: daily-ci + docker: huggingface/transformers-all-latest-gpu + ci_event: Daily CI + secrets: inherit + + torch-pipeline: + name: Torch pipeline CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_pipelines_torch_gpu + slack_report_channel: "#transformers-ci-daily-pipeline-torch" + runner: daily-ci + docker: huggingface/transformers-pytorch-gpu + ci_event: Daily CI + secrets: inherit + + tf-pipeline: + name: TF pipeline CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_pipelines_tf_gpu + slack_report_channel: "#transformers-ci-daily-pipeline-tf" + runner: daily-ci + docker: huggingface/transformers-tensorflow-gpu + ci_event: Daily CI + secrets: inherit + + example-ci: + name: Example CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_examples_gpu + slack_report_channel: "#transformers-ci-daily-examples" + runner: daily-ci + docker: huggingface/transformers-all-latest-gpu + ci_event: Daily CI + secrets: inherit + + deepspeed-ci: + name: DeepSpeed CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_torch_cuda_extensions_gpu + slack_report_channel: "#transformers-ci-daily-deepspeed" + runner: daily-ci + docker: huggingface/transformers-pytorch-deepspeed-latest-gpu + ci_event: Daily CI + working-directory-prefix: /workspace + secrets: inherit + + quantization-ci: + name: Quantization CI + uses: ./.github/workflows/self-scheduled.yml + with: + job: run_quantization_torch_gpu + slack_report_channel: "#transformers-ci-daily-quantization" + runner: daily-ci + docker: huggingface/transformers-quantization-latest-gpu + ci_event: Daily CI + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled.yml new file mode 100644 index 0000000000000000000000000000000000000000..78971820d146de19a345362e8b05c377206a724d --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/self-scheduled.yml @@ -0,0 +1,573 @@ +name: Self-hosted runner (scheduled) + +# Note that each job's dependencies go into a corresponding docker file. +# +# For example for `run_torch_cuda_extensions_gpu` the docker image is +# `huggingface/transformers-pytorch-deepspeed-latest-gpu`, which can be found at +# `docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile` + +on: + workflow_call: + inputs: + job: + required: true + type: string + slack_report_channel: + required: true + type: string + runner: + required: true + type: string + docker: + required: true + type: string + ci_event: + required: true + type: string + working-directory-prefix: + default: '' + required: false + type: string + +env: + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes + # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. + # This token is created under the bot `hf-transformers-bot`. + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + CUDA_VISIBLE_DEVICES: 0,1 + NUM_SLICES: 2 + +jobs: + setup: + if: contains(fromJSON('["run_models_gpu", "run_quantization_torch_gpu"]'), inputs.job) + name: Setup + strategy: + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + outputs: + folder_slices: ${{ steps.set-matrix.outputs.folder_slices }} + slice_ids: ${{ steps.set-matrix.outputs.slice_ids }} + quantization_matrix: ${{ steps.set-matrix-quantization.outputs.quantization_matrix }} + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - id: set-matrix + if: ${{ inputs.job == 'run_models_gpu' }} + name: Identify models to test + working-directory: /transformers/tests + run: | + echo "folder_slices=$(python3 ../utils/split_model_tests.py --num_splits ${{ env.NUM_SLICES }})" >> $GITHUB_OUTPUT + echo "slice_ids=$(python3 -c 'd = list(range(${{ env.NUM_SLICES }})); print(d)')" >> $GITHUB_OUTPUT + + - id: set-matrix-quantization + if: ${{ inputs.job == 'run_quantization_torch_gpu' }} + name: Identify quantization method to test + working-directory: /transformers/tests + run: | + echo "quantization_matrix=$(python3 -c 'import os; tests = os.getcwd(); quantization_tests = os.listdir(os.path.join(tests, "quantization")); d = sorted(list(filter(os.path.isdir, [f"quantization/{x}" for x in quantization_tests]))) ; print(d)')" >> $GITHUB_OUTPUT + + - name: NVIDIA-SMI + run: | + nvidia-smi + + run_models_gpu: + if: ${{ inputs.job == 'run_models_gpu' }} + name: " " + needs: setup + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + slice_id: ${{ fromJSON(needs.setup.outputs.slice_ids) }} + uses: ./.github/workflows/model_jobs.yml + with: + folder_slices: ${{ needs.setup.outputs.folder_slices }} + machine_type: ${{ matrix.machine_type }} + slice_id: ${{ matrix.slice_id }} + runner: ${{ inputs.runner }} + docker: ${{ inputs.docker }} + secrets: inherit + + run_pipelines_torch_gpu: + if: ${{ inputs.job == 'run_pipelines_torch_gpu' }} + name: PyTorch pipelines + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-pytorch-gpu + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run all pipeline tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports tests/pipelines + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_pipelines_torch_gpu_test_reports + + run_pipelines_tf_gpu: + if: ${{ inputs.job == 'run_pipelines_tf_gpu' }} + name: TensorFlow pipelines + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-tensorflow-gpu + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run all pipeline tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports tests/pipelines + + - name: Failure short reports + if: ${{ always() }} + run: | + cat /transformers/reports/${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_pipelines_tf_gpu_test_reports + + run_examples_gpu: + if: ${{ inputs.job == 'run_examples_gpu' }} + name: Examples directory + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-all-latest-gpu + options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run examples tests on GPU + working-directory: /transformers + run: | + pip install -r examples/pytorch/_tests_requirements.txt + python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_examples_gpu_test_reports examples/pytorch + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_examples_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_examples_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_examples_gpu_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_examples_gpu_test_reports + + run_torch_cuda_extensions_gpu: + if: ${{ inputs.job == 'run_torch_cuda_extensions_gpu' }} + name: Torch CUDA extension tests + strategy: + fail-fast: false + matrix: + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: ${{ inputs.docker }} + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Update clone + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: Update / Install some packages (for Past CI) + if: ${{ contains(inputs.docker, '-past-') && contains(inputs.docker, '-pytorch-') }} + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: | + python3 -m pip install -U datasets + python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + + - name: Remove cached torch extensions + run: rm -rf /github/home/.cache/torch_extensions/ + + # To avoid unknown test failures + - name: Pre build DeepSpeed *again* (for daily CI) + if: ${{ contains(inputs.ci_event, 'Daily CI') }} + working-directory: ${{ inputs.working-directory-prefix }}/ + run: | + python3 -m pip uninstall -y deepspeed + DS_DISABLE_NINJA=1 DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check + + # To avoid unknown test failures + - name: Pre build DeepSpeed *again* (for nightly & Past CI) + if: ${{ contains(inputs.ci_event, 'Nightly CI') || contains(inputs.ci_event, 'Past CI') }} + working-directory: ${{ inputs.working-directory-prefix }}/ + run: | + python3 -m pip uninstall -y deepspeed + rm -rf DeepSpeed + git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build + DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run all tests on GPU + working-directory: ${{ inputs.working-directory-prefix }}/transformers + run: | + python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports tests/deepspeed tests/extended + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat ${{ inputs.working-directory-prefix }}/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + path: ${{ inputs.working-directory-prefix }}/transformers/reports/${{ env.machine_type }}_run_torch_cuda_extensions_gpu_test_reports + + run_quantization_torch_gpu: + if: ${{ inputs.job == 'run_quantization_torch_gpu' }} + name: " " + needs: setup + strategy: + max-parallel: 4 + fail-fast: false + matrix: + folders: ${{ fromJson(needs.setup.outputs.quantization_matrix) }} + machine_type: [aws-g4dn-2xlarge-cache, aws-g4dn-12xlarge-cache] + runs-on: + group: '${{ matrix.machine_type }}' + container: + image: huggingface/transformers-quantization-latest-gpu + options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + steps: + - name: Echo folder ${{ matrix.folders }} + shell: bash + run: | + echo "${{ matrix.folders }}" + matrix_folders=${{ matrix.folders }} + matrix_folders=${matrix_folders/'quantization/'/'quantization_'} + echo "$matrix_folders" + echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV + + - name: Update clone + working-directory: /transformers + run: git fetch && git checkout ${{ github.sha }} + + - name: Reinstall transformers in edit mode (remove the one installed during docker image build) + working-directory: /transformers + run: python3 -m pip uninstall -y transformers && python3 -m pip install -e . + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Environment + working-directory: /transformers + run: | + python3 utils/print_env.py + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: Set `machine_type` for report and artifact names + working-directory: /transformers + shell: bash + run: | + echo "${{ matrix.machine_type }}" + + if [ "${{ matrix.machine_type }}" = "aws-g4dn-2xlarge-cache" ]; then + machine_type=single-gpu + elif [ "${{ matrix.machine_type }}" = "aws-g4dn-12xlarge-cache" ]; then + machine_type=multi-gpu + else + machine_type=${{ matrix.machine_type }} + fi + + echo "$machine_type" + echo "machine_type=$machine_type" >> $GITHUB_ENV + + - name: Run quantization tests on GPU + working-directory: /transformers + run: | + python3 -m pytest -v --make-reports=${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports tests/${{ matrix.folders }} + + - name: Failure short reports + if: ${{ failure() }} + continue-on-error: true + run: cat /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports/failures_short.txt + + - name: "Test suite reports artifacts: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports" + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: ${{ env.machine_type }}_run_quantization_torch_gpu_${{ env.matrix_folders }}_test_reports + path: /transformers/reports/${{ env.machine_type }}_run_quantization_torch_gpu_${{ matrix.folders }}_test_reports + + run_extract_warnings: + # Let's only do this for the job `run_models_gpu` to simplify the (already complex) logic. + if: ${{ always() && inputs.job == 'run_models_gpu' }} + name: Extract warnings in CI artifacts + runs-on: ubuntu-22.04 + needs: [setup, run_models_gpu] + steps: + - name: Checkout transformers + uses: actions/checkout@v4 + with: + fetch-depth: 2 + + - name: Install transformers + run: pip install transformers + + - name: Show installed libraries and their versions + run: pip freeze + + - name: Create output directory + run: mkdir warnings_in_ci + + - uses: actions/download-artifact@v4 + with: + path: warnings_in_ci + + - name: Show artifacts + run: echo "$(python3 -c 'import os; d = os.listdir(); print(d)')" + working-directory: warnings_in_ci + + - name: Extract warnings in CI artifacts + run: | + python3 utils/extract_warnings.py --workflow_run_id ${{ github.run_id }} --output_dir warnings_in_ci --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }} --from_gh + echo "$(python3 -c 'import os; import json; fp = open("warnings_in_ci/selected_warnings.json"); d = json.load(fp); d = "\n".join(d) ;print(d)')" + + - name: Upload artifact + if: ${{ always() }} + uses: actions/upload-artifact@v4 + with: + name: warnings_in_ci + path: warnings_in_ci/selected_warnings.json + + send_results: + name: Slack Report + needs: [ + setup, + run_models_gpu, + run_pipelines_torch_gpu, + run_pipelines_tf_gpu, + run_examples_gpu, + run_torch_cuda_extensions_gpu, + run_quantization_torch_gpu, + run_extract_warnings + ] + if: ${{ always() }} + uses: ./.github/workflows/slack-report.yml + with: + job: ${{ inputs.job }} + # This would be `skipped` if `setup` is skipped. + setup_status: ${{ needs.setup.result }} + slack_report_channel: ${{ inputs.slack_report_channel }} + # This would be an empty string if `setup` is skipped. + folder_slices: ${{ needs.setup.outputs.folder_slices }} + quantization_matrix: ${{ needs.setup.outputs.quantization_matrix }} + ci_event: ${{ inputs.ci_event }} + + secrets: inherit + + check_new_model_failures: + if: ${{ always() && inputs.ci_event == 'Daily CI' && inputs.job == 'run_models_gpu' && needs.send_results.result == 'success' }} + name: Check new model failures + needs: send_results + uses: ./.github/workflows/check_failed_model_tests.yml + with: + docker: ${{ inputs.docker }} + start_sha: ${{ github.sha }} + secrets: inherit diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/slack-report.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/slack-report.yml new file mode 100644 index 0000000000000000000000000000000000000000..cbea37ff567a96272cfc5f8d8c4cfd6a8947b87c --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/slack-report.yml @@ -0,0 +1,101 @@ +name: CI slack report + +on: + workflow_call: + inputs: + job: + required: true + type: string + slack_report_channel: + required: true + type: string + setup_status: + required: true + type: string + folder_slices: + required: true + type: string + quantization_matrix: + required: true + type: string + ci_event: + required: true + type: string + +env: + TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN: ${{ secrets.TRANSFORMERS_CI_RESULTS_UPLOAD_TOKEN }} + +jobs: + send_results: + name: Send results to webhook + runs-on: ubuntu-22.04 + if: always() + steps: + - name: Preliminary job status + shell: bash + # For the meaning of these environment variables, see the job `Setup` + run: | + echo "Setup status: ${{ inputs.setup_status }}" + + - uses: actions/checkout@v4 + - uses: actions/download-artifact@v4 + - name: Send message to Slack + if: ${{ inputs.job != 'run_quantization_torch_gpu' }} + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }} + CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }} + CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }} + SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + CI_EVENT: ${{ inputs.ci_event }} + CI_SHA: ${{ github.sha }} + CI_WORKFLOW_REF: ${{ github.workflow_ref }} + CI_TEST_JOB: ${{ inputs.job }} + SETUP_STATUS: ${{ inputs.setup_status }} + # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change + # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`. + # For a job that doesn't depend on (i.e. `needs`) `setup`, the value for `inputs.folder_slices` would be an + # empty string, and the called script still get one argument (which is the emtpy string). + run: | + sudo apt-get install -y curl + pip install huggingface_hub + pip install slack_sdk + pip show slack_sdk + python utils/notification_service.py "${{ inputs.folder_slices }}" + + # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack. + - name: Failure table artifacts + uses: actions/upload-artifact@v4 + with: + name: ci_results_${{ inputs.job }} + path: ci_results_${{ inputs.job }} + + - uses: actions/checkout@v4 + - uses: actions/download-artifact@v4 + - name: Send message to Slack for quantization workflow + if: ${{ inputs.job == 'run_quantization_torch_gpu' }} + env: + CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }} + ACCESS_REPO_INFO_TOKEN: ${{ secrets.ACCESS_REPO_INFO_TOKEN }} + SLACK_REPORT_CHANNEL: ${{ inputs.slack_report_channel }} + CI_EVENT: ${{ inputs.ci_event }} + CI_SHA: ${{ github.sha }} + CI_TEST_JOB: ${{ inputs.job }} + SETUP_STATUS: ${{ inputs.setup_status }} + # We pass `needs.setup.outputs.quantization_matrix` as the argument. A processing in `notification_service_quantization.py` to change + # `quantization/bnb` to `quantization_bnb` is required, as the artifact names use `_` instead of `/`. + run: | + sudo apt-get install -y curl + pip install huggingface_hub + pip install slack_sdk + pip show slack_sdk + python utils/notification_service_quantization.py "${{ inputs.quantization_matrix }}" + + # Upload complete failure tables, as they might be big and only truncated versions could be sent to Slack. + - name: Failure table artifacts + if: ${{ inputs.job == 'run_quantization_torch_gpu' }} + uses: actions/upload-artifact@v4 + with: + name: ci_results_${{ inputs.job }} + path: ci_results_${{ inputs.job }} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/ssh-runner.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/ssh-runner.yml new file mode 100644 index 0000000000000000000000000000000000000000..e648883f191e0da240c5e72fc09496564b0a2891 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/ssh-runner.yml @@ -0,0 +1,113 @@ +name: SSH into our runners + +on: + workflow_dispatch: + inputs: + runner_type: + description: 'Type of runner to test (a10 or t4)' + required: true + docker_image: + description: 'Name of the Docker image' + required: true + num_gpus: + description: 'Type of the number of gpus to use (`single` or `multi`)' + required: true + +env: + HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }} + HF_HOME: /mnt/cache + TRANSFORMERS_IS_CI: yes + OMP_NUM_THREADS: 8 + MKL_NUM_THREADS: 8 + RUN_SLOW: yes # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. # This token is created under the bot `hf-transformers-bot`. + SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }} + TF_FORCE_GPU_ALLOW_GROWTH: true + CUDA_VISIBLE_DEVICES: 0,1 + +jobs: + get_runner: + name: "Get runner to use" + runs-on: ubuntu-22.04 + outputs: + RUNNER: ${{ steps.set_runner.outputs.RUNNER }} + steps: + - name: Get runner to use + shell: bash + run: | + if [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then + echo "RUNNER=aws-g4dn-2xlarge-cache" >> $GITHUB_ENV + elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "t4" ]]; then + echo "RUNNER=aws-g4dn-12xlarge-cache" >> $GITHUB_ENV + elif [[ "${{ github.event.inputs.num_gpus }}" == "single" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then + echo "RUNNER=aws-g5-4xlarge-cache" >> $GITHUB_ENV + elif [[ "${{ github.event.inputs.num_gpus }}" == "multi" && "${{ github.event.inputs.runner_type }}" == "a10" ]]; then + echo "RUNNER=aws-g5-12xlarge-cache" >> $GITHUB_ENV + else + echo "RUNNER=" >> $GITHUB_ENV + fi + + - name: Set runner to use + id: set_runner + run: | + echo ${{ env.RUNNER }} + echo "RUNNER=${{ env.RUNNER }}" >> $GITHUB_OUTPUT + + ssh_runner: + name: "SSH" + needs: get_runner + runs-on: + group: ${{ needs.get_runner.outputs.RUNNER }} + container: + image: ${{ github.event.inputs.docker_image }} + options: --gpus all --privileged --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/ + + steps: + - name: Update clone + working-directory: /transformers + run: | + git fetch && git checkout ${{ github.sha }} + + - name: Cleanup + working-directory: /transformers + run: | + rm -rf tests/__pycache__ + rm -rf tests/models/__pycache__ + rm -rf reports + + - name: Show installed libraries and their versions + working-directory: /transformers + run: pip freeze + + - name: NVIDIA-SMI + run: | + nvidia-smi + + - name: Store Slack infos + #because the SSH can be enabled dynamically if the workflow failed, so we need to store slack infos to be able to retrieve them during the waitforssh step + shell: bash + run: | + echo "${{ github.actor }}" + github_actor=${{ github.actor }} + github_actor=${github_actor/'-'/'_'} + echo "$github_actor" + echo "github_actor=$github_actor" >> $GITHUB_ENV + + - name: Store Slack infos + #because the SSH can be enabled dynamically if the workflow failed, so we need to store slack infos to be able to retrieve them during the waitforssh step + shell: bash + run: | + echo "${{ env.github_actor }}" + if [ "${{ secrets[format('{0}_{1}', env.github_actor, 'SLACK_ID')] }}" != "" ]; then + echo "SLACKCHANNEL=${{ secrets[format('{0}_{1}', env.github_actor, 'SLACK_ID')] }}" >> $GITHUB_ENV + else + echo "SLACKCHANNEL=${{ secrets.SLACK_CIFEEDBACK_CHANNEL }}" >> $GITHUB_ENV + fi + + - name: Tailscale # In order to be able to SSH when a test fails + uses: huggingface/tailscale-action@main + with: + authkey: ${{ secrets.TAILSCALE_SSH_AUTHKEY }} + slackChannel: ${{ env.SLACKCHANNEL }} + slackToken: ${{ secrets.SLACK_CIFEEDBACK_BOT_TOKEN }} + waitForSSH: true + sshTimeout: 15m diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/stale.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/stale.yml new file mode 100644 index 0000000000000000000000000000000000000000..65eaf755ab3a69f9d978f8ab6f7e14584918262c --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/stale.yml @@ -0,0 +1,29 @@ +name: Stale Bot + +on: + schedule: + - cron: "0 8 * * *" + +jobs: + close_stale_issues: + name: Close Stale Issues + if: github.repository == 'huggingface/transformers' + runs-on: ubuntu-22.04 + permissions: + issues: write + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v4 + + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version: 3.8 + + - name: Install requirements + run: | + pip install PyGithub + - name: Close stale issues + run: | + python scripts/stale.py diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/trufflehog.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/trufflehog.yml new file mode 100644 index 0000000000000000000000000000000000000000..7f6646e114d82c20dfba79f0d625a02e1ee3522d --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/trufflehog.yml @@ -0,0 +1,20 @@ +on: + push: + +name: Secret Leaks + +permissions: + contents: read + +jobs: + trufflehog: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Secret Scanning + uses: trufflesecurity/trufflehog@main + with: + extra_args: --results=verified,unknown diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/update_metdata.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/update_metdata.yml new file mode 100644 index 0000000000000000000000000000000000000000..90cd73077ac0bb01ef4b14b2305ff4d39d612eba --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/update_metdata.yml @@ -0,0 +1,27 @@ +name: Update Transformers metadata + +on: + push: + branches: + - main + - update_transformers_metadata* + +jobs: + build_and_package: + runs-on: ubuntu-22.04 + defaults: + run: + shell: bash -l {0} + + steps: + - uses: actions/checkout@v4 + + - name: Setup environment + run: | + pip install --upgrade pip + pip install datasets pandas==2.0.3 + pip install .[torch,tf,flax] + + - name: Update metadata + run: | + python utils/update_metadata.py --token ${{ secrets.LYSANDRE_HF_TOKEN }} --commit_sha ${{ github.sha }} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/upload_pr_documentation.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/upload_pr_documentation.yml new file mode 100644 index 0000000000000000000000000000000000000000..64befc595c421e1167765404e551b080211cb192 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.github/workflows/upload_pr_documentation.yml @@ -0,0 +1,16 @@ +name: Upload PR Documentation + +on: + workflow_run: + workflows: ["Build PR Documentation"] + types: + - completed + +jobs: + build: + uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main + with: + package_name: transformers + secrets: + hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }} + comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }} \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/.gitignore b/tasks/tasksmith-e0f68dd4420c/environment/source/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..337f2ef2c735e89dc8f76ba3367cc29301264f66 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/.gitignore @@ -0,0 +1,169 @@ +# Initially taken from Github's Python gitignore file + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# tests and logs +tests/fixtures/cached_*_text.txt +logs/ +lightning_logs/ +lang_code_data/ + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# vscode +.vs +.vscode + +# Pycharm +.idea + +# TF code +tensorflow_code + +# Models +proc_data + +# examples +runs +/runs_old +/wandb +/examples/runs +/examples/**/*.args +/examples/rag/sweep + +# data +/data +serialization_dir + +# emacs +*.*~ +debug.env + +# vim +.*.swp + +#ctags +tags + +# pre-commit +.pre-commit* + +# .lock +*.lock + +# DS_Store (MacOS) +.DS_Store + +# ruff +.ruff_cache diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/CITATION.cff b/tasks/tasksmith-e0f68dd4420c/environment/source/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..b4d5156786f94df7ca0aebe5322b17dbb1521cb3 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/CITATION.cff @@ -0,0 +1,82 @@ +cff-version: "1.2.0" +date-released: 2020-10 +message: "If you use this software, please cite it using these metadata." +title: "Transformers: State-of-the-Art Natural Language Processing" +url: "https://github.com/huggingface/transformers" +authors: + - family-names: Wolf + given-names: Thomas + - family-names: Debut + given-names: Lysandre + - family-names: Sanh + given-names: Victor + - family-names: Chaumond + given-names: Julien + - family-names: Delangue + given-names: Clement + - family-names: Moi + given-names: Anthony + - family-names: Cistac + given-names: Perric + - family-names: Ma + given-names: Clara + - family-names: Jernite + given-names: Yacine + - family-names: Plu + given-names: Julien + - family-names: Xu + given-names: Canwen + - family-names: "Le Scao" + given-names: Teven + - family-names: Gugger + given-names: Sylvain + - family-names: Drame + given-names: Mariama + - family-names: Lhoest + given-names: Quentin + - family-names: Rush + given-names: "Alexander M." +preferred-citation: + type: conference-paper + authors: + - family-names: Wolf + given-names: Thomas + - family-names: Debut + given-names: Lysandre + - family-names: Sanh + given-names: Victor + - family-names: Chaumond + given-names: Julien + - family-names: Delangue + given-names: Clement + - family-names: Moi + given-names: Anthony + - family-names: Cistac + given-names: Perric + - family-names: Ma + given-names: Clara + - family-names: Jernite + given-names: Yacine + - family-names: Plu + given-names: Julien + - family-names: Xu + given-names: Canwen + - family-names: "Le Scao" + given-names: Teven + - family-names: Gugger + given-names: Sylvain + - family-names: Drame + given-names: Mariama + - family-names: Lhoest + given-names: Quentin + - family-names: Rush + given-names: "Alexander M." + booktitle: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations" + month: 10 + start: 38 + end: 45 + title: "Transformers: State-of-the-Art Natural Language Processing" + year: 2020 + publisher: "Association for Computational Linguistics" + url: "https://www.aclweb.org/anthology/2020.emnlp-demos.6" + address: "Online" diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/CODE_OF_CONDUCT.md b/tasks/tasksmith-e0f68dd4420c/environment/source/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..b23f3150a5a6987a74acc7bb31df8c9bf1a48d57 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/CODE_OF_CONDUCT.md @@ -0,0 +1,133 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +feedback@huggingface.co. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/CONTRIBUTING.md b/tasks/tasksmith-e0f68dd4420c/environment/source/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..c6047b0e1cc1ac2a9d7dd6222f4ee28dd0156910 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/CONTRIBUTING.md @@ -0,0 +1,395 @@ + + +# Contribute to 🤗 Transformers + +Everyone is welcome to contribute, and we value everybody's contribution. Code +contributions are not the only way to help the community. Answering questions, helping +others, and improving the documentation are also immensely valuable. + +It also helps us if you spread the word! Reference the library in blog posts +about the awesome projects it made possible, shout out on Twitter every time it has +helped you, or simply ⭐️ the repository to say thank you. + +However you choose to contribute, please be mindful and respect our +[code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md). + +**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).** + +## Ways to contribute + +There are several ways you can contribute to 🤗 Transformers: + +* Fix outstanding issues with the existing code. +* Submit issues related to bugs or desired new features. +* Implement new models. +* Contribute to the examples or to the documentation. + +If you don't know where to start, there is a special [Good First +Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of +open issues that are beginner-friendly and help you start contributing to open-source. The best way to do that is to open a Pull Request and link it to the issue that you'd like to work on. We try to give priority to opened PRs as we can easily track the progress of the fix, and if the contributor does not have time anymore, someone else can take the PR over. + +For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/transformers/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀 + +> All contributions are equally valuable to the community. 🥰 + +## Fixing outstanding issues + +If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](#create-a-pull-request) and open a Pull Request! + +## Submitting a bug-related issue or feature request + +Do your best to follow these guidelines when submitting a bug-related issue or a feature +request. It will make it easier for us to come back to you quickly and with good +feedback. + +### Did you find a bug? + +The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter. + +Before you report an issue, we would really appreciate it if you could **make sure the bug was not +already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask in the [forum](https://discuss.huggingface.co/) or on our [discord](https://discord.com/invite/hugging-face-879548962464493619) first. This helps us respond quicker to fixing issues related to the library versus general questions. + +> [!TIP] +> We have a [docs bot](https://huggingface.co/spaces/huggingchat/hf-docs-chat), and we highly encourage you to ask all your questions there. There is always a chance your bug can be fixed with a simple flag 👾🔫 + +Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it: + +* Your **OS type and version** and **Python**, **PyTorch** and + **TensorFlow** versions when applicable. +* A short, self-contained, code snippet that allows us to reproduce the bug in + less than 30s. +* The *full* traceback if an exception is raised. +* Attach any other additional information, like screenshots, you think may help. + +To get the OS and software versions automatically, run the following command: + +```bash +transformers-cli env +``` + +You can also run the same command from the root of the repository: + +```bash +python src/transformers/commands/transformers_cli.py env +``` + +### Do you want a new feature? + +If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe: + +1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community? + + Whatever it is, we'd love to hear about it! + +2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you. +3. Provide a *code snippet* that demonstrates the features usage. +4. If the feature is related to a paper, please include a link. + +If your issue is well written we're already 80% of the way there by the time you create it. + +We have added [templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with your issue. + +## Do you want to implement a new model? + +New models are constantly released and if you want to implement a new model, please provide the following information: + +* A short description of the model and a link to the paper. +* Link to the implementation if it is open-sourced. +* Link to the model weights if they are available. + +If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers! + +We have a technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model). + +## Do you want to add documentation? + +We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested! + +For more details about how to generate, build, and write the documentation, take a look at the documentation [README](https://github.com/huggingface/transformers/tree/main/docs). + +## Create a Pull Request + +Before writing any code, we strongly advise you to search through the existing PRs or +issues to make sure nobody is already working on the same thing. If you are +unsure, it is always a good idea to open an issue to get some feedback. + +You will need basic `git` proficiency to contribute to +🤗 Transformers. While `git` is not the easiest tool to use, it has the greatest +manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro +Git](https://git-scm.com/book/en/v2) is a very good reference. + +You'll need **[Python 3.9](https://github.com/huggingface/transformers/blob/main/setup.py#L449)** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing: + +1. Fork the [repository](https://github.com/huggingface/transformers) by + clicking on the **[Fork](https://github.com/huggingface/transformers/fork)** button on the repository's page. This creates a copy of the code + under your GitHub user account. + +2. Clone your fork to your local disk, and add the base repository as a remote: + + ```bash + git clone git@github.com:/transformers.git + cd transformers + git remote add upstream https://github.com/huggingface/transformers.git + ``` + +3. Create a new branch to hold your development changes: + + ```bash + git checkout -b a-descriptive-name-for-my-changes + ``` + + 🚨 **Do not** work on the `main` branch! + +4. Set up a development environment by running the following command in a virtual environment: + + ```bash + pip install -e ".[dev]" + ``` + + If 🤗 Transformers was already installed in the virtual environment, remove + it with `pip uninstall transformers` before reinstalling it in editable + mode with the `-e` flag. + + Depending on your OS, and since the number of optional dependencies of Transformers is growing, you might get a + failure with this command. If that's the case make sure to install the Deep Learning framework you are working with + (PyTorch, TensorFlow and/or Flax) then do: + + ```bash + pip install -e ".[quality]" + ``` + + which should be enough for most use cases. + +5. Develop the features in your branch. + + As you work on your code, you should make sure the test suite + passes. Run the tests impacted by your changes like this: + + ```bash + pytest tests/.py + ``` + + For more information about tests, check out the + [Testing](https://huggingface.co/docs/transformers/testing) guide. + + 🤗 Transformers relies on `black` and `ruff` to format its source code + consistently. After you make changes, apply automatic style corrections and code verifications + that can't be automated in one go with: + + ```bash + make fixup + ``` + + This target is also optimized to only work with files modified by the PR you're working on. + + If you prefer to run the checks one after the other, the following command applies the + style corrections: + + ```bash + make style + ``` + + 🤗 Transformers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality + controls are run by the CI, but you can run the same checks with: + + ```bash + make quality + ``` + + Finally, we have a lot of scripts to make sure we don't forget to update + some files when adding a new model. You can run these scripts with: + + ```bash + make repo-consistency + ``` + + To learn more about those checks and how to fix any issues with them, check out the + [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. + + If you're modifying documents under the `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check + make sure you install the documentation builder: + + ```bash + pip install ".[docs]" + ``` + + Run the following command from the root of the repository: + + ```bash + doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build + ``` + + This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated + Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request. + + Once you're happy with your changes, add the changed files with `git add` and + record your changes locally with `git commit`: + + ```bash + git add modified_file.py + git commit + ``` + + Please remember to write [good commit + messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made! + + To keep your copy of the code up to date with the original + repository, rebase your branch on `upstream/branch` *before* you open a pull request or if requested by a maintainer: + + ```bash + git fetch upstream + git rebase upstream/main + ``` + + Push your changes to your branch: + + ```bash + git push -u origin a-descriptive-name-for-my-changes + ``` + + If you've already opened a pull request, you'll need to force push with the `--force` flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally. + +6. Now you can go to your fork of the repository on GitHub and click on **Pull Request** to open a pull request. Make sure you tick off all the boxes on our [checklist](#pull-request-checklist) below. When you're ready, you can send your changes to the project maintainers for review. + +7. It's ok if maintainers request changes, it happens to our core contributors + too! So everyone can see the changes in the pull request, work in your local + branch and push the changes to your fork. They will automatically appear in + the pull request. + +### Pull request checklist + +☐ The pull request title should summarize your contribution.
+☐ If your pull request addresses an issue, please mention the issue number in the pull +request description to make sure they are linked (and people viewing the issue know you +are working on it).
+☐ To indicate a work in progress please prefix the title with `[WIP]`. These are +useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.
+☐ Make sure existing tests pass.
+☐ If adding a new feature, also add tests for it.
+ - If you are adding a new model, make sure you use + `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger the common tests. + - If you are adding new `@slow` tests, make sure they pass using + `RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`. + - If you are adding a new tokenizer, write tests and make sure + `RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes. + - CircleCI does not run the slow tests, but GitHub Actions does every night!
+ +☐ All public methods must have informative docstrings (see +[`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py) +for an example).
+☐ Due to the rapidly growing repository, don't add any images, videos and other +non-text files that'll significantly weigh down the repository. Instead, use a Hub +repository such as [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) +to host these files and reference them by URL. We recommend placing documentation +related images in the following repository: +[huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). +You can open a PR on this dataset repository and ask a Hugging Face member to merge it. + +For more information about the checks run on a pull request, take a look at our [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. + +### Tests + +An extensive test suite is included to test the library behavior and several examples. Library tests can be found in +the [tests](https://github.com/huggingface/transformers/tree/main/tests) folder and examples tests in the +[examples](https://github.com/huggingface/transformers/tree/main/examples) folder. + +We like `pytest` and `pytest-xdist` because it's faster. From the root of the +repository, specify a *path to a subfolder or a test file* to run the test: + +```bash +python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model +``` + +Similarly, for the `examples` directory, specify a *path to a subfolder or test file* to run the test. For example, the following command tests the text classification subfolder in the PyTorch `examples` directory: + +```bash +pip install -r examples/xxx/requirements.txt # only needed the first time +python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification +``` + +In fact, this is actually how our `make test` and `make test-examples` commands are implemented (not including the `pip install`)! + +You can also specify a smaller set of tests in order to test only the feature +you're working on. + +By default, slow tests are skipped but you can set the `RUN_SLOW` environment variable to +`yes` to run them. This will download many gigabytes of models so make sure you +have enough disk space, a good internet connection or a lot of patience! + + + +Remember to specify a *path to a subfolder or a test file* to run the test. Otherwise, you'll run all the tests in the `tests` or `examples` folder, which will take a very long time! + + + +```bash +RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model +RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification +``` + +Like the slow tests, there are other environment variables available which are not enabled by default during testing: +- `RUN_CUSTOM_TOKENIZERS`: Enables tests for custom tokenizers. + +More environment variables and additional information can be found in the [testing_utils.py](https://github.com/huggingface/transformers/blob/main/src/transformers/testing_utils.py). + +🤗 Transformers uses `pytest` as a test runner only. It doesn't use any +`pytest`-specific features in the test suite itself. + +This means `unittest` is fully supported. Here's how to run tests with +`unittest`: + +```bash +python -m unittest discover -s tests -t . -v +python -m unittest discover -s examples -t examples -v +``` + +### Style guide + +For documentation strings, 🤗 Transformers follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). +Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification) +for more information. + +### Develop on Windows + +On Windows (unless you're working in [Windows Subsystem for Linux](https://learn.microsoft.com/en-us/windows/wsl/) or WSL), you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings: + +```bash +git config core.autocrlf input +``` + +One way to run the `make` command on Windows is with MSYS2: + +1. [Download MSYS2](https://www.msys2.org/), and we assume it's installed in `C:\msys64`. +2. Open the command line `C:\msys64\msys2.exe` (it should be available from the **Start** menu). +3. Run in the shell: `pacman -Syu` and install `make` with `pacman -S make`. +4. Add `C:\msys64\usr\bin` to your PATH environment variable. + +You can now use `make` from any terminal (PowerShell, cmd.exe, etc.)! 🎉 + +### Sync a forked repository with upstream main (the Hugging Face repository) + +When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs. + +1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. +2. If a PR is absolutely necessary, use the following steps after checking out your branch: + + ```bash + git checkout -b your-branch-for-syncing + git pull --squash --no-commit upstream main + git commit -m '' + git push --set-upstream origin your-branch-for-syncing + ``` diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0e38f6b27ba84438e440e27ef494509841c6d07a --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/Dockerfile @@ -0,0 +1,8 @@ +FROM python:3.12-slim +WORKDIR /workspace +RUN python -m pip install --no-cache-dir torch==2.11.0+cpu --extra-index-url https://download.pytorch.org/whl/cpu numpy==2.5.3 packaging==26.3 parameterized==0.9.0 pytest==8.4.2 setuptools==78.1.0 wheel==0.48.0 tokenizers==0.21.4 huggingface-hub==0.28.1 safetensors==0.5.3 regex==2026.9.10 requests==2.34.2 filelock==3.32.6 Pillow==12.3.0 tqdm==4.70.1 PyYAML==6.0.3 certifi==2026.7.22 charset-normalizer==3.5.1 idna==3.19 urllib3==2.7.0 typing_extensions==4.16.0 datasets==2.21.0 dill==0.3.8 multiprocess==0.70.16 xxhash==4.0.1 pyarrow==25.0.1 fsspec==2024.6.1 pandas==3.0.5 python-dateutil==2.9.0.post0 six==1.17.0 aiohappyeyeballs==2.7.1 aiohttp==3.14.3 aiosignal==1.4.0 attrs==26.1.0 frozenlist==1.8.0 multidict==6.8.0 propcache==0.5.2 yarl==1.24.5 +COPY . /workspace +RUN python -m pip install --no-cache-dir --no-deps --no-build-isolation -e . +RUN rm -rf /workspace/.git /root/.cache/pip +ENV PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 +ENV HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 HF_DATASETS_OFFLINE=1 diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/ISSUES.md b/tasks/tasksmith-e0f68dd4420c/environment/source/ISSUES.md new file mode 100644 index 0000000000000000000000000000000000000000..a5969a3027f86dc1ed20ec1683642a1c723d0d19 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/ISSUES.md @@ -0,0 +1,277 @@ + + +# How To Request Support + +This is an Open Source Project so please be mindful that like in any other project of this kind there is no obligation to answer all requests for help. + +However, we want to encourage you to ask for help whenever you think it's needed! We are happy about every question we get because it allows us to better understand your needs, possible misunderstandings, and most importantly a way for you to help us make this library better. That being said, this document's main purpose is to provide guidelines at how you can formulate your requests to increase your chances to be understood and to get support. + +There are two main venues to receive support: [the forums](https://discuss.huggingface.co/) and [the GitHub issues](https://github.com/huggingface/transformers/issues). + +## The Forums + +[The user forums](https://discuss.huggingface.co/) are supported by the wide community of the library users and backed up by developers when needed. + +If you have a difficulty with deploying this library or some questions, or you'd like to discuss a new feature, please first consider discussing those things at the forums. Only when you feel your subject matter has been crystalized and you still need support from the library developers do proceed to file an [issue](https://github.com/huggingface/transformers/issues). + +In particular all "Please explain" questions or objectively very user-specific feature requests belong to the forums. Here are some example of such questions: + +* "I would like to use a BertModel within a RL-Agent for a customer support service. How can I use a BertForMaskedLM in my ChatBotModel?" + +* "Could you please explain why T5 has no positional embedding matrix under T5Model?" + +* "How should I set my generation parameters for translation?" + +* "How to train T5 on De->En translation?" + + +## The GitHub Issues + +Everything which hints at a bug should be opened as an [issue](https://github.com/huggingface/transformers/issues). + +You are not required to read the following guidelines before opening an issue. However, if you notice that your issue doesn't get any replies, chances are that the developers have one or several difficulties with its quality. In this case, reading the following points and adjusting your issue accordingly could help. + +1. Before posting an issue, first search for already posted issues, since chances are someone has already asked a similar question before you. + + If you use Google your search query should be: + + ``` + "huggingface" "transformers" your query + ``` + + The first two quoted words tell Google to limit the search to the context of the Huggingface Transformers. The remainder is your query - most commonly this would be the error message the software fails with. We will go deeper into details shortly. + + The results of such a query will typically match GitHub issues, Hugging Face forums, StackExchange, and blogs. + + If you find relevant hints, you may choose to continue the discussion there if you have follow up questions. + + If what you found is similar but doesn't quite answer your problem, please, post a new issue and do include links to similar issues or forum discussions you may have found. + + Let's look at some examples: + + The error message, often referred to as an assertion, tells us what went wrong. Here is an example of an assertion: + + ```python + Traceback (most recent call last): + File "", line 1, in + File "/transformers/src/transformers/__init__.py", line 34, in + from . import dependency_versions_check + File "/transformers/src/transformers/dependency_versions_check.py", line 34, in + from .utils import is_tokenizers_available + File "/transformers/src/transformers/utils/import_utils.py", line 40, in + from tqdm.auto import tqdm + ModuleNotFoundError: No module named 'tqdm.auto' + ``` + + and it typically includes a traceback, so that we can see the full stack of calls the program made before it fails. This gives us the context to know why the program failed. + + Going back to the above example. If you received this error search, look at the very last line of the error which is: + + ```python + ModuleNotFoundError: No module named 'tqdm.auto' + ``` + + And now we can use it to do the searching on your favorite search engine: + + 1. first for `"huggingface" "transformers" "ModuleNotFoundError: No module named 'tqdm.auto'"` + 2. if you don't find relevant results, then search for just `"ModuleNotFoundError: No module named 'tqdm.auto'"` + 3. and finally if nothing still comes up, then remove the outside quotes: `ModuleNotFoundError: No module named 'tqdm.auto'` + + If the error includes any messages that include bits unique to your filesystem, always remove those in the search query since other users will not have the same filesystem as yours. For example: + + ```bash + python -c 'open("/tmp/wrong_path.txt", "r")' + Traceback (most recent call last): + File "", line 1, in + FileNotFoundError: [Errno 2] No such file or directory: '/tmp/wrong_path.txt' + ``` + Here you'd search for just: `"FileNotFoundError: [Errno 2] No such file or directory"` + + If the local information that you removed were inside the error message and you removed them you may need to remove double quotes since your query is no longer exact. So if the error message was something like: + + ```bash + ValueError: '/tmp/wrong_path.txt' cannot be found + ``` + + then you'd search for `"ValueError" "cannot be found"` + + As you search you will notice that when you don't use quotes often the search engines will return a variety of unrelated hits, which may or may not be what you want. + + Experiment with different ways and find which approach gives the most satisfactory results. + +2. Keep the issue short, providing the information that you think will aid the developers to understand your situation. Put yourself in the shoes of the person who has never seen your code or knows anything about your custom setup. This mental exercise will help to develop an intuition to what/what not to share" + +3. If there is a software failure, always provide the full traceback, for example: + + ```python + $ python -c 'import transformers' + Traceback (most recent call last): + File "", line 1, in + File "/transformers/src/transformers/__init__.py", line 34, in + from . import dependency_versions_check + File "/transformers/src/transformers/dependency_versions_check.py", line 34, in + from .utils import is_tokenizers_available + File "/transformers/src/transformers/utils/import_utils.py", line 40, in + from tqdm.auto import tqdm + ModuleNotFoundError: No module named 'tqdm.auto' + ``` + + As compared to providing just the last line of the error message, e.g.: + ```python + ModuleNotFoundError: No module named 'tqdm.auto' + ``` + which is not sufficient. + + If your application is running on more than one GPU (e.g. under `DistributedDataParallel`) and typically getting every log and traceback printed multiple times, please make sure that you paste only one copy of it. At times the traceback from parallel processes may get interleaved - so either disentangle these or change the loggers to log only for `local_rank==0` so that only one process logs things. + +4. When quoting a traceback, command line instructions and any type of code always enclose it in triple backticks inside the editor window, that is: + + ```` + ``` + git clone https://github.com/huggingface/transformers + cd transformers + pip install . + ``` + ```` + + If it's a command line with a long argument list, please consider breaking it down using backslashes and new lines. Here is an example of a good command line quote: + + ```bash + cd examples/seq2seq + torchrun --nproc_per_node=2 ./finetune_trainer.py \ + --model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \ + --output_dir output_dir --overwrite_output_dir \ + --do_train --n_train 500 --num_train_epochs 1 \ + --per_device_train_batch_size 1 --freeze_embeds \ + --src_lang en_XX --tgt_lang ro_RO --task translation \ + --fp16 + ``` + + If you don't break it up, one has to scroll horizontally which often makes it quite difficult to quickly see what's happening. + + The backslashes allow us to copy the command directly into the console to run it, without needing to edit it. + +5. Include only the important information that you think will help the developer to quickly identify the problem. + + For example applications often create huge amounts of logs. Ask yourself whether providing all or parts of the log is useful. + + Pasting a 100-1000 lines of log into the issue is an immediate turn off, since it will take a lot of time to figure out where the pertinent parts of the log are. + + Attaching a full log can be helpful if it's done as an attachment, if it's enclosed in the following html code in the comment editor window: + + ``` +
+ Full log + 
+
+   many
+   lines
+   go
+   here
+
+
+
+ ``` + + which would result in the following entry, which can be opened if desired, but otherwise takes little space. + +
+ Full log + 
+   many
+   lines
+   go
+   here
+
+
+ + You could also provide a link to a pastebin service, but this is less beneficial since those links tend to expire quickly and future readers of your issue might not be able to access that log file anymore and may lack some context. + +6. If this is an issue in your code, do try to reduce that code to a minimal example that still demonstrates the problem. Please ask at the forums if you have a hard time figuring how to do that. Please realize that we don't have the luxury of having time to try and understand all of your custom code. + + If you really tried to make a short reproducible code but couldn't figure it out, it might be that having a traceback will give the developer enough information to know what's going on. But if it is not enough and we can't reproduce the problem, we can't really solve it. + + Do not despair if you can't figure it out from the beginning, just share what you can and perhaps someone else will be able to help you at the forums. + + If your setup involves any custom datasets, the best way to help us reproduce the problem is to create a [Google Colab notebook](https://colab.research.google.com/) that demonstrates the issue and once you verify that the issue still exists, include a link to that notebook in the Issue. Just make sure that you don't copy and paste the location bar url of the open notebook - as this is private and we won't be able to open it. Instead, you need to click on `Share` in the right upper corner of the notebook, select `Get Link` and then copy and paste the public link it will give to you. + +7. If you forked off some of this project's code or example applications, please, do not ask us to go into your code repository and figure out what you may have done. The code is already very complex and unless there is an easy way to do a diff and it's a small diff, it won't be possible to find someone with time on their hands to make a lengthy investigation. Albeit, you might find someone at the forums who will be generous to do this for you. + +8. Before reporting an issue, first, always try to update your environment to the latest official version of this library. We have no resources to go and debug older revisions, which could easily have bugs that have been fixed in the latest released version. + + We understand that this is not always possible, especially when APIs change, in which case file an issue against the highest library version your environment can support. + + Of course, if you upgrade the library, always retest that the problem is still there. + +9. Please do not ask us to reproduce an issue with your custom data, since we don't have it. So, either you should use some existing dataset supported by HF datasets or you need to supply a code that generates a small sample on the fly, or some another quick and simple way to get it. + + Please do not send us any non-public domain data that may require a license or a permission to be used. + +10. Do not tag multiple developers on the issue unless you know this is expected, either because you asked them and they gave you an explicit permission to tag them or the issue template instructs you to do so. + + The "who to tag for what domain" part of the issue template is there to help users direct their questions to the right developers who are designated maintainers of project's specific domains. They can then decide at their own discretion to tag other developers if they feel it'd help move the issue forward. + + We currently don't have a triage service and we trust your capacity to identify the right domain and thus the persons to tag in your issue. If you are not sure, please use the forums to ask for guidance. + + When in doubt, err on the side of not tagging a given person. If you tag multiple people out of context or permission don't be surprised if you get no response at all. Please remember that every time you tag someone, they get a notification and you're taking their time without their permission. Please be sensitive to that. + + If you got helped by one of the developers in the past please don't tag them in future issues, unless they are listed in the issue template for the domain you are asking about or that developer gave you an explicit permission to tag them in future issues. + + If you see a certain developer doing multiple and/or recent commits into a specific area of the project that you feel is relevant to your issue, it is not a good reason to tag them. Various developers may be fixing things that prevent them from moving forward, but often their work is focused on a totally different domain. And while they may or may not know how to help you with the problem at hand, it would benefit the whole community much more if they focus on the domain of their unique expertise. + +11. Use the Edit button. Take your time, and re-read and improve the wording and formatting to make your posts and comments as easy to understand as possible. + + Avoid posting multiple comments in a row, as each comment generates a notification for the developers tagged in that issue. If you happened to post multiple comments in a row, and nobody followed up yet - consider merging those into one or a few comments while editing the combined content to be coherent. + + If you choose to edit your older comments after others posted follow up comments you need to be aware that your modifications might not be noticed, so if it's not a typo fixing, try to write a new comment flagging that something has been changed in the previous comments. + + For example, the very first comment is the most important one. If while the thread unfolds you realize that things aren't as they seemed to you originally you may want to edit the first post to reflect the up-to-date understanding of the issue at hand so that it helps those who read your issue in the future quickly understand what's going on and not need to sift through dozens of comments. It also helps to indicate that the post was edited. So, those reading the thread later can understand why there might be certain discontinuity in the information flow. + + Use bullets and items if you have lists of items and the outcome improves overall readability. + + Use backticks to refer to class and function names, e.g. `BartModel` and `generate` as these stand out and improve the speed of a reader's comprehension. + + Try not use italics and bold text too much as these often make the text more difficult to read. + + +12. If you are cross-referencing a specific comment in a given thread or another issue, always link to that specific comment, rather than using the issue link. If you do the latter it could be quite impossible to find which specific comment you're referring to. + + To get the link to the specific comment do not copy the url from the location bar of your browser, but instead, click the `...` icon in the upper right corner of the comment and then select "Copy Link". + + For example the first link is a link to an issue, and the second to a specific comment in the same issue: + + 1. https://github.com/huggingface/transformers/issues/9257 + 2. https://github.com/huggingface/transformers/issues/9257#issuecomment-749945162 + + +13. If you are replying to a last comment, it's totally fine to make your reply with just your comment in it. The readers can follow the information flow here. + + But if you're replying to a comment that happened some comments back it's always a good practice to quote just the relevant lines you're replying it. The `>` is used for quoting, or you can always use the menu to do so. For example your editor box will look like: + + ``` + > How big is your gpu cluster? + + Our cluster is made of 256 gpus. + ``` + + If you are addressing multiple comments, quote the relevant parts of each before your answer. Some people use the same comment to do multiple replies, others separate them into separate comments. Either way works. The latter approach helps for linking to a specific comment. + +In general the best way to figure out what works the best is learn from issues posted by other people - see which issues get great responses and which get little to no response - observe what the posters who received great responses did differently from those who did not. + +Thank you for reading this somewhat lengthy document. We would like to conclude that these are not absolute rules, but a friendly advice that will help maximize the chances for us to understand what you are trying to communicate, reproduce the problem then resolve it to your satisfaction and the benefit of the whole community. + +If after reading this document there are remaining questions on how and why or there is a need for further elucidation, please, don't hesitate to ask your question in [this thread](https://discuss.huggingface.co/t/how-to-request-support/3128). diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/LICENSE b/tasks/tasksmith-e0f68dd4420c/environment/source/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..68b7d66c97d66c58de883ed0c451af2b3183e6f3 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/LICENSE @@ -0,0 +1,203 @@ +Copyright 2018- The Hugging Face team. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/Makefile b/tasks/tasksmith-e0f68dd4420c/environment/source/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..21152e985082ace05f8cc083b8161f4298309b28 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/Makefile @@ -0,0 +1,127 @@ +.PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples benchmark + +# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!) +export PYTHONPATH = src + +check_dirs := examples tests src utils + +exclude_folders := "" + +modified_only_fixup: + $(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs))) + @if test -n "$(modified_py_files)"; then \ + echo "Checking/fixing $(modified_py_files)"; \ + ruff check $(modified_py_files) --fix --exclude $(exclude_folders); \ + ruff format $(modified_py_files) --exclude $(exclude_folders);\ + else \ + echo "No library .py files were modified"; \ + fi + +# Update src/transformers/dependency_versions_table.py + +deps_table_update: + @python setup.py deps_table_update + +deps_table_check_updated: + @md5sum src/transformers/dependency_versions_table.py > md5sum.saved + @python setup.py deps_table_update + @md5sum -c --quiet md5sum.saved || (printf "\nError: the version dependency table is outdated.\nPlease run 'make fixup' or 'make style' and commit the changes.\n\n" && exit 1) + @rm md5sum.saved + +# autogenerating code + +autogenerate_code: deps_table_update + +# Check that the repo is in a good state + +repo-consistency: + python utils/check_copies.py + python utils/check_modular_conversion.py + python utils/check_dummies.py + python utils/check_repo.py + python utils/check_inits.py + python utils/check_config_docstrings.py + python utils/check_config_attributes.py + python utils/check_doctest_list.py + python utils/update_metadata.py --check-only + python utils/check_docstrings.py + +# this target runs checks on all files + +quality: + @python -c "from transformers import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) + ruff check $(check_dirs) setup.py conftest.py + ruff format --check $(check_dirs) setup.py conftest.py + python utils/sort_auto_mappings.py --check_only + python utils/check_doc_toc.py + python utils/check_docstrings.py --check_all + + +# Format source code automatically and check is there are any problems left that need manual fixing + +extra_style_checks: + python utils/sort_auto_mappings.py + python utils/check_doc_toc.py --fix_and_overwrite + +# this target runs checks on all files and potentially modifies some of them + +style: + ruff check $(check_dirs) setup.py conftest.py --fix --exclude $(exclude_folders) + ruff format $(check_dirs) setup.py conftest.py --exclude $(exclude_folders) + ${MAKE} autogenerate_code + ${MAKE} extra_style_checks + +# Super fast fix and check target that only works on relevant modified files since the branch was made + +fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency + +# Make marked copies of snippets of codes conform to the original + +fix-copies: + python utils/check_copies.py --fix_and_overwrite + python utils/check_modular_conversion.py --fix_and_overwrite + python utils/check_dummies.py --fix_and_overwrite + python utils/check_doctest_list.py --fix_and_overwrite + python utils/check_docstrings.py --fix_and_overwrite + +# Run tests for the library + +test: + python -m pytest -n auto --dist=loadfile -s -v ./tests/ + +# Run tests for examples + +test-examples: + python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/ + +# Run benchmark + +benchmark: + python3 benchmark/benchmark.py --config-dir benchmark/config --config-name generation --commit=diff backend.model=google/gemma-2b backend.cache_implementation=null,static backend.torch_compile=false,true --multirun + +# Run tests for SageMaker DLC release + +test-sagemaker: # install sagemaker dependencies in advance with pip install .[sagemaker] + TEST_SAGEMAKER=True python -m pytest -n auto -s -v ./tests/sagemaker + + +# Release stuff + +pre-release: + python utils/release.py + +pre-patch: + python utils/release.py --patch + +post-release: + python utils/release.py --post_release + +post-patch: + python utils/release.py --post_release --patch + +build-release: + rm -rf dist + rm -rf build + python setup.py bdist_wheel + python setup.py sdist + python utils/check_build.py diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/README.md b/tasks/tasksmith-e0f68dd4420c/environment/source/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6bbcdbc82f8bf6f162c922e03935d0c7c3b95ae3 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/README.md @@ -0,0 +1,342 @@ + + +

+ + + + Hugging Face Transformers Library + +
+
+

+ +

+ Build + GitHub + Documentation + GitHub release + Contributor Covenant + DOI +

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+

+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +

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State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow

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+ +

+ +🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. + +These models can be applied on: + +* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages. +* 🖼️ Images, for tasks like image classification, object detection, and segmentation. +* 🗣️ Audio, for tasks like speech recognition and audio classification. + +Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering. + +🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. + +🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other. + +## Online demos + +You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models. + +Here are a few examples: + +In Natural Language Processing: +- [Masked word completion with BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France) +- [Named Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city) +- [Text generation with Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) +- [Natural Language Inference with RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal) +- [Summarization with BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct) +- [Question answering with DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species) +- [Translation with T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin) + +In Computer Vision: +- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224) +- [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50) +- [Semantic Segmentation with SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) +- [Panoptic Segmentation with Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic) +- [Depth Estimation with Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) +- [Video Classification with VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae) +- [Universal Segmentation with OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large) + +In Audio: +- [Automatic Speech Recognition with Whisper](https://huggingface.co/openai/whisper-large-v3) +- [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks) +- [Audio Classification with Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) + +In Multimodal tasks: +- [Table Question Answering with TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq) +- [Visual Question Answering with ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa) +- [Image captioning with LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf) +- [Zero-shot Image Classification with SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) +- [Document Question Answering with LayoutLM](https://huggingface.co/impira/layoutlm-document-qa) +- [Zero-shot Video Classification with X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip) +- [Zero-shot Object Detection with OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2) +- [Zero-shot Image Segmentation with CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg) +- [Automatic Mask Generation with SAM](https://huggingface.co/docs/transformers/model_doc/sam) + + +## 100 projects using Transformers + +Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the +Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone +else to build their dream projects. + +In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the +community, and we have created the [awesome-transformers](./awesome-transformers.md) page which lists 100 +incredible projects built in the vicinity of transformers. + +If you own or use a project that you believe should be part of the list, please open a PR to add it! + +## Serious about AI in your organisation? Build faster with the Hugging Face Enterprise Hub. + + + Hugging Face Enterprise Hub +
+ +## Quick tour + +To immediately use a model on a given input (text, image, audio, ...), we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts: + +```python +>>> from transformers import pipeline + +# Allocate a pipeline for sentiment-analysis +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +The second line of code downloads and caches the pretrained model used by the pipeline, while the third evaluates it on the given text. Here, the answer is "positive" with a confidence of 99.97%. + +Many tasks have a pre-trained `pipeline` ready to go, in NLP but also in computer vision and speech. For example, we can easily extract detected objects in an image: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download an image with cute cats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Allocate a pipeline for object detection +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Here, we get a list of objects detected in the image, with a box surrounding the object and a confidence score. Here is the original image on the left, with the predictions displayed on the right: + +

+ + +

+ +You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/docs/transformers/task_summary). + +In addition to `pipeline`, to download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version: +```python +>>> from transformers import AutoTokenizer, AutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="pt") +>>> outputs = model(**inputs) +``` + +And here is the equivalent code for TensorFlow: +```python +>>> from transformers import AutoTokenizer, TFAutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="tf") +>>> outputs = model(**inputs) +``` + +The tokenizer is responsible for all the preprocessing the pretrained model expects and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator. + +The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use as usual. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset. + +## Why should I use transformers? + +1. Easy-to-use state-of-the-art models: + - High performance on natural language understanding & generation, computer vision, and audio tasks. + - Low barrier to entry for educators and practitioners. + - Few user-facing abstractions with just three classes to learn. + - A unified API for using all our pretrained models. + +1. Lower compute costs, smaller carbon footprint: + - Researchers can share trained models instead of always retraining. + - Practitioners can reduce compute time and production costs. + - Dozens of architectures with over 400,000 pretrained models across all modalities. + +1. Choose the right framework for every part of a model's lifetime: + - Train state-of-the-art models in 3 lines of code. + - Move a single model between TF2.0/PyTorch/JAX frameworks at will. + - Seamlessly pick the right framework for training, evaluation, and production. + +1. Easily customize a model or an example to your needs: + - We provide examples for each architecture to reproduce the results published by its original authors. + - Model internals are exposed as consistently as possible. + - Model files can be used independently of the library for quick experiments. + +## Why shouldn't I use transformers? + +- This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files. +- The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library (possibly, [Accelerate](https://huggingface.co/docs/accelerate)). +- While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/main/examples) are just that: examples. It is expected that they won't work out-of-the-box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs. + +## Installation + +### With pip + +This repository is tested on Python 3.9+, Flax 0.4.1+, PyTorch 2.0+, and TensorFlow 2.6+. + +You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/). + +First, create a virtual environment with the version of Python you're going to use and activate it. + +**macOS/Linux** + +```python -m venv env +source env/bin/activate +``` + +**Windows** + +``` python -m venv env +env\Scripts\activate +``` + +To use 🤗 Transformers, you must install at least one of Flax, PyTorch, or TensorFlow. Refer to the official installation guides for platform-specific commands: + +[TensorFlow installation page](https://www.tensorflow.org/install/), +[PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) + +When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows: + +``` +pip install transformers +``` + +If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source). + +``` +git clone https://github.com/huggingface/transformers.git +cd transformers +pip install . +``` + +### With conda + +🤗 Transformers can be installed using conda as follows: + +```shell script +conda install conda-forge::transformers +``` + +> **_NOTE:_** Installing `transformers` from the `huggingface` channel is deprecated. + +Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda. + +> **_NOTE:_** On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062). + +## Model architectures + +**[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co/models), where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations). + +Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen) + +🤗 Transformers currently provides the following architectures: see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them. + +To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks). + +These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://github.com/huggingface/transformers/tree/main/examples). + + +## Learn more + +| Section | Description | +|-|-| +| [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials | +| [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers | +| [Preprocessing tutorial](https://huggingface.co/docs/transformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models | +| [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API | +| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks | +| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community | + +## Citation + +We now have a [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) you can cite for the 🤗 Transformers library: +```bibtex +@inproceedings{wolf-etal-2020-transformers, + title = "Transformers: State-of-the-Art Natural Language Processing", + author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", + pages = "38--45" +} +``` diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/SECURITY.md b/tasks/tasksmith-e0f68dd4420c/environment/source/SECURITY.md new file mode 100644 index 0000000000000000000000000000000000000000..431b17a85042dccdd3062941b0e26099ef38a4b2 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/SECURITY.md @@ -0,0 +1,39 @@ +# Security Policy + +## Hugging Face Hub, remote artefacts, and remote code + +Transformers is open-source software that is tightly coupled to the Hugging Face Hub. While you have the ability to use it +offline with pre-downloaded model weights, it provides a very simple way to download, use, and manage models locally. + +When downloading artefacts that have been uploaded by others on any platform, you expose yourself to risks. Please +read below for the security recommendations in order to keep your runtime and local environment safe. + +### Remote artefacts + +Models uploaded on the Hugging Face Hub come in different formats. We heavily recommend uploading and downloading +models in the [`safetensors`](https://github.com/huggingface/safetensors) format (which is the default prioritized +by the transformers library), as developed specifically to prevent arbitrary code execution on your system. + +To avoid loading models from unsafe formats(e.g. [pickle](https://docs.python.org/3/library/pickle.html), you should use the `use_safetensors` parameter. If doing so, in the event that no .safetensors file is present, transformers will error when loading the model. + +### Remote code + +#### Modeling + +Transformers supports many model architectures, but is also the bridge between your Python runtime and models that +are stored in model repositories on the Hugging Face Hub. + +These models require the `trust_remote_code=True` parameter to be set when using them; please **always** verify +the content of the modeling files when using this argument. We recommend setting a revision in order to ensure you +protect yourself from updates on the repository. + +#### Tools + +Through the `Agent` framework, remote tools can be downloaded to be used by the Agent. You're to specify these tools +yourself, but please keep in mind that their code will be run on your machine if the Agent chooses to run them. + +Please inspect the code of the tools before passing them to the Agent to protect your runtime and local setup. + +## Reporting a Vulnerability + +Feel free to submit vulnerability reports to [security@huggingface.co](mailto:security@huggingface.co), where someone from the HF security team will review and recommend next steps. If reporting a vulnerability specific to open source, please note [Huntr](https://huntr.com) is a vulnerability disclosure program for open source software. diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/awesome-transformers.md b/tasks/tasksmith-e0f68dd4420c/environment/source/awesome-transformers.md new file mode 100644 index 0000000000000000000000000000000000000000..f9676f29b28c13953bd7ae203d19702a79d0a4e7 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/awesome-transformers.md @@ -0,0 +1,609 @@ +# Awesome projects built with Transformers + +This page lists awesome projects built on top of Transformers. Transformers is more than a toolkit to use pretrained +models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable +developers, researchers, students, professors, engineers, and anyone else to build their dream projects. + +In this list, we showcase incredibly impactful and novel projects that have pushed the field forward. We celebrate +100 of these projects as we reach the milestone of 100k stars as a community; but we're very open to pull requests +adding other projects to the list. If you believe a project should be here and it's not, then please, open a PR +to add it. + +## [gpt4all](https://github.com/nomic-ai/gpt4all) + +[gpt4all](https://github.com/nomic-ai/gpt4all) is an ecosystem of open-source chatbots trained on massive collections of clean assistant data including code, stories and dialogue. It offers open-source, large language models such as LLaMA and GPT-J trained in an assistant-style. + +Keywords: Open-source, LLaMa, GPT-J, instruction, assistant + +## [recommenders](https://github.com/recommenders-team/recommenders) + +This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It goes over several aspects required to build efficient recommendation systems: data preparation, modeling, evaluation, model selection & optimization, as well as operationalization + +Keywords: Recommender systems, AzureML + +## [IOPaint](https://github.com/Sanster/IOPaint) + +Image inpainting tool powered by Stable Diffusion. Remove any unwanted object, defect, people from your pictures or erase and replace anything on your pictures. + +Keywords: inpainting, SD, Stable Diffusion + +## [flair](https://github.com/flairNLP/flair) + +FLAIR is a powerful PyTorch NLP framework, covering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things. + +Keywords: NLP, text embedding, document embedding, biomedical, NER, PoS, sentiment-analysis + +## [mindsdb](https://github.com/mindsdb/mindsdb) + +MindsDB is a low-code ML platform, which automates and integrates several ML frameworks into the data stack as "AI Tables" to streamline the integration of AI into applications, making it accessible to developers of all skill levels. + +Keywords: Database, low-code, AI table + +## [langchain](https://github.com/langchain-ai/langchain) + +[langchain](https://github.com/langchain-ai/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools. + +Keywords: LLMs, Large Language Models, Agents, Chains + +## [LlamaIndex](https://github.com/run-llama/llama_index) + +[LlamaIndex](https://github.com/run-llama/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results. + +Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation + +## [ParlAI](https://github.com/facebookresearch/ParlAI) + +[ParlAI](https://github.com/facebookresearch/ParlAI) is a python framework for sharing, training and testing dialogue models, from open-domain chitchat, to task-oriented dialogue, to visual question answering. It provides more than 100 datasets under the same API, a large zoo of pretrained models, a set of agents, and has several integrations. + +Keywords: Dialogue, Chatbots, VQA, Datasets, Agents + +## [sentence-transformers](https://github.com/UKPLab/sentence-transformers) + +This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity. + +Keywords: Dense vector representations, Text embeddings, Sentence embeddings + +## [ludwig](https://github.com/ludwig-ai/ludwig) + +Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. Ludwig is targeted at a wide variety of AI tasks. It provides a data-driven configuration system, training, prediction, and evaluation scripts, as well as a programmatic API. + +Keywords: Declarative, Data-driven, ML Framework + +## [InvokeAI](https://github.com/invoke-ai/InvokeAI) + +[InvokeAI](https://github.com/invoke-ai/InvokeAI) is an engine for Stable Diffusion models, aimed at professionals, artists, and enthusiasts. It leverages the latest AI-driven technologies through CLI as well as a WebUI. + +Keywords: Stable-Diffusion, WebUI, CLI + +## [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) + +[PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) is an easy-to-use and powerful NLP library particularly targeted at the Chinese languages. It has support for multiple pre-trained model zoos, and supports a wide-range of NLP tasks from research to industrial applications. + +Keywords: NLP, Chinese, Research, Industry + +## [stanza](https://github.com/stanfordnlp/stanza) + +The Stanford NLP Group's official Python NLP library. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. + +Keywords: NLP, Multilingual, CoreNLP + +## [DeepPavlov](https://github.com/deeppavlov/DeepPavlov) + +[DeepPavlov](https://github.com/deeppavlov/DeepPavlov) is an open-source conversational AI library. It is designed for the development of production ready chat-bots and complex conversational systems, as well as research in the area of NLP and, particularly, of dialog systems. + +Keywords: Conversational, Chatbot, Dialog + +## [alpaca-lora](https://github.com/tloen/alpaca-lora) + +Alpaca-lora contains code for reproducing the Stanford Alpaca results using low-rank adaptation (LoRA). The repository provides training (fine-tuning) as well as generation scripts. + +Keywords: LoRA, Parameter-efficient fine-tuning + +## [imagen-pytorch](https://github.com/lucidrains/imagen-pytorch) + +An open-source Implementation of Imagen, Google's closed-source Text-to-Image Neural Network that beats DALL-E2. As of release, it is the new SOTA for text-to-image synthesis. + +Keywords: Imagen, Text-to-image + +## [adapters](https://github.com/adapter-hub/adapters) + +[adapters](https://github.com/adapter-hub/adapters) is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules. It is a drop-in replacement for transformers, which is regularly updated to stay up-to-date with the developments of transformers. + +Keywords: Adapters, LoRA, Parameter-efficient fine-tuning, Hub + +## [NeMo](https://github.com/NVIDIA/NeMo) + +NVIDIA [NeMo](https://github.com/NVIDIA/NeMo) is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR), text-to-speech synthesis (TTS), large language models (LLMs), and natural language processing (NLP). The primary objective of [NeMo](https://github.com/NVIDIA/NeMo) is to help researchers from industry and academia to reuse prior work (code and pretrained models) and make it easier to create new https://developer.nvidia.com/conversational-ai#started. + +Keywords: Conversational, ASR, TTS, LLMs, NLP + +## [Runhouse](https://github.com/run-house/runhouse) + +[Runhouse](https://github.com/run-house/runhouse) allows to send code and data to any of your compute or data infra, all in Python, and continue to interact with them normally from your existing code and environment. Runhouse developers mention: + +> Think of it as an expansion pack to your Python interpreter that lets it take detours to remote machines or manipulate remote data. + +Keywords: MLOps, Infrastructure, Data storage, Modeling + +## [MONAI](https://github.com/Project-MONAI/MONAI) + +[MONAI](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its ambitions are: +- developing a community of academic, industrial and clinical researchers collaborating on a common foundation; +- creating state-of-the-art, end-to-end training workflows for healthcare imaging; +- providing researchers with the optimized and standardized way to create and evaluate deep learning models. + +Keywords: Healthcare imaging, Training, Evaluation + +## [simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers) + +Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize, train, and evaluate a model. It supports a wide variety of NLP tasks. + +Keywords: Framework, simplicity, NLP + +## [JARVIS](https://github.com/microsoft/JARVIS) + +[JARVIS](https://github.com/microsoft/JARVIS) is a system attempting to merge LLMs such as GPT-4 with the rest of the open-source ML community: leveraging up to 60 downstream models in order to perform tasks identified by the LLM. + +Keywords: LLM, Agents, HF Hub + +## [transformers.js](https://github.com/huggingface/transformers.js/) + +[transformers.js](https://github.com/huggingface/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser. + +Keywords: Transformers, JavaScript, browser + +## [bumblebee](https://github.com/elixir-nx/bumblebee) + +Bumblebee provides pre-trained Neural Network models on top of Axon, a neural networks library for the Elixir language. It includes integration with 🤗 Models, allowing anyone to download and perform Machine Learning tasks with few lines of code. + +Keywords: Elixir, Axon + +## [argilla](https://github.com/argilla-io/argilla) + +Argilla is an open-source platform providing advanced NLP labeling, monitoring, and workspaces. It is compatible with many open source ecosystems such as Hugging Face, Stanza, FLAIR, and others. + +Keywords: NLP, Labeling, Monitoring, Workspaces + +## [haystack](https://github.com/deepset-ai/haystack) + +Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs. It offers production-ready tools to quickly build complex decision making, question answering, semantic search, text generation applications, and more. + +Keywords: NLP, Framework, LLM + +## [spaCy](https://github.com/explosion/spaCy) + +[spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It offers support for transformers models through its third party package, spacy-transformers. + +Keywords: NLP, Framework + +## [speechbrain](https://github.com/speechbrain/speechbrain) + +SpeechBrain is an open-source and all-in-one conversational AI toolkit based on PyTorch. +The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, speech separation, language identification, multi-microphone signal processing, and many others. + +Keywords: Conversational, Speech + +## [skorch](https://github.com/skorch-dev/skorch) + +Skorch is a scikit-learn compatible neural network library that wraps PyTorch. It has support for models within transformers, and tokenizers from tokenizers. + +Keywords: Scikit-Learn, PyTorch + +## [bertviz](https://github.com/jessevig/bertviz) + +BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. + +Keywords: Visualization, Transformers + +## [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) + +[mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) is a haiku library using the xmap/pjit operators in JAX for model parallelism of transformers. This library is designed for scalability up to approximately 40B parameters on TPUv3s. It was the library used to train the GPT-J model. + +Keywords: Haiku, Model parallelism, LLM, TPU + +## [deepchem](https://github.com/deepchem/deepchem) + +DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, quantum chemistry, and biology. + +Keywords: Drug discovery, Materials Science, Quantum Chemistry, Biology + +## [OpenNRE](https://github.com/thunlp/OpenNRE) + +An Open-Source Package for Neural Relation Extraction (NRE). It is targeted at a wide range of users, from newcomers to relation extraction, to developers, researchers, or students. + +Keywords: Neural Relation Extraction, Framework + +## [pycorrector](https://github.com/shibing624/pycorrector) + +PyCorrector is a Chinese Text Error Correction Tool. It uses a language model to detect errors, pinyin feature and shape feature to correct Chinese text errors. it can be used for Chinese Pinyin and stroke input method. + +Keywords: Chinese, Error correction tool, Language model, Pinyin + +## [nlpaug](https://github.com/makcedward/nlpaug) + +This python library helps you with augmenting nlp for machine learning projects. It is a lightweight library featuring synthetic data generation for improving model performance, support for audio and text, and compatibility with several ecosystems (scikit-learn, pytorch, tensorflow). + +Keywords: Data augmentation, Synthetic data generation, Audio, NLP + +## [dream-textures](https://github.com/carson-katri/dream-textures) + +[dream-textures](https://github.com/carson-katri/dream-textures) is a library targeted at bringing stable-diffusion support within Blender. It supports several use-cases, such as image generation, texture projection, inpainting/outpainting, ControlNet, and upscaling. + +Keywords: Stable-Diffusion, Blender + +## [seldon-core](https://github.com/SeldonIO/seldon-core) + +Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices. +Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries and more. + +Keywords: Microservices, Modeling, Language wrappers + +## [open_model_zoo](https://github.com/openvinotoolkit/open_model_zoo) + +This repository includes optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications. Use these free pre-trained models instead of training your own models to speed-up the development and production deployment process. + +Keywords: Optimized models, Demos + +## [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion) + +ML-Stable-Diffusion is a repository by Apple bringing Stable Diffusion support to Core ML, on Apple Silicon devices. It supports stable diffusion checkpoints hosted on the Hugging Face Hub. + +Keywords: Stable Diffusion, Apple Silicon, Core ML + +## [stable-dreamfusion](https://github.com/ashawkey/stable-dreamfusion) + +Stable-Dreamfusion is a pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model. + +Keywords: Text-to-3D, Stable Diffusion + +## [txtai](https://github.com/neuml/txtai) + +[txtai](https://github.com/neuml/txtai) is an open-source platform for semantic search and workflows powered by language models. txtai builds embeddings databases, which are a union of vector indexes and relational databases enabling similarity search with SQL. Semantic workflows connect language models together into unified applications. + +Keywords: Semantic search, LLM + +## [djl](https://github.com/deepjavalibrary/djl) + +Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for developers. DJL provides a native Java development experience and functions like any other regular Java library. DJL offers [a Java binding](https://github.com/deepjavalibrary/djl/tree/master/extensions/tokenizers) for HuggingFace Tokenizers and easy conversion toolkit for HuggingFace model to deploy in Java. + +Keywords: Java, Framework + +## [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/) + +This project provides a unified framework to test generative language models on a large number of different evaluation tasks. It has support for more than 200 tasks, and supports different ecosystems: HF Transformers, GPT-NeoX, DeepSpeed, as well as the OpenAI API. + +Keywords: LLM, Evaluation, Few-shot + +## [gpt-neox](https://github.com/EleutherAI/gpt-neox) + +This repository records EleutherAI's library for training large-scale language models on GPUs. The framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. It is focused on training multi-billion-parameter models. + +Keywords: Training, LLM, Megatron, DeepSpeed + +## [muzic](https://github.com/microsoft/muzic) + +Muzic is a research project on AI music that empowers music understanding and generation with deep learning and artificial intelligence. Muzic was created by researchers from Microsoft Research Asia. + +Keywords: Music understanding, Music generation + +## [dalle-flow](https://github.com/jina-ai/dalle-flow) + +DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. Itt leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt. +The preferred candidate is fed to GLID-3 XL for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via SwinIR. + +Keywords: High-definition image generation, Stable Diffusion, DALL-E Mega, GLID-3 XL, CLIP, SwinIR + +## [lightseq](https://github.com/bytedance/lightseq) + +LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA. It enables highly efficient computation of modern NLP and CV models such as BERT, GPT, Transformer, etc. It is therefore best useful for machine translation, text generation, image classification, and other sequence related tasks. + +Keywords: Training, Inference, Sequence Processing, Sequence Generation + +## [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) + +The goal of this project is to create a learning based system that takes an image of a math formula and returns corresponding LaTeX code. + +Keywords: OCR, LaTeX, Math formula + +## [open_clip](https://github.com/mlfoundations/open_clip) + +OpenCLIP is an open source implementation of OpenAI's CLIP. + +The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift. +The starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset. + +Specifically, a ResNet-50 model trained with this codebase on OpenAI's 15 million image subset of YFCC achieves 32.7% top-1 accuracy on ImageNet. + +Keywords: CLIP, Open-source, Contrastive, Image-text + +## [dalle-playground](https://github.com/saharmor/dalle-playground) + +A playground to generate images from any text prompt using Stable Diffusion and Dall-E mini. + +Keywords: WebUI, Stable Diffusion, Dall-E mini + +## [FedML](https://github.com/FedML-AI/FedML) + +[FedML](https://github.com/FedML-AI/FedML) is a federated learning and analytics library enabling secure and collaborative machine learning on decentralized data anywhere at any scale. + +It supports large-scale cross-silo federated learning, and cross-device federated learning on smartphones/IoTs, and research simulation. + +Keywords: Federated Learning, Analytics, Collaborative ML, Decentralized + +## [gpt-code-clippy](https://github.com/CodedotAl/gpt-code-clippy) + +GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub. + +Keywords: LLM, Code + +## [TextAttack](https://github.com/QData/TextAttack) + +[TextAttack](https://github.com/QData/TextAttack) 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP. + +Keywords: Adversarial attacks, Data augmentation, NLP + +## [OpenPrompt](https://github.com/thunlp/OpenPrompt) + +Prompt-learning is a paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modify the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. This library provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. [OpenPrompt](https://github.com/thunlp/OpenPrompt) supports loading PLMs directly from https://github.com/huggingface/transformers. + +## [text-generation-webui](https://github.com/oobabooga/text-generation-webui/) + +[text-generation-webui](https://github.com/oobabooga/text-generation-webui/) is a Gradio Web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. + +Keywords: LLM, WebUI + +## [libra](https://github.com/Palashio/libra) + +An ergonomic machine learning [libra](https://github.com/Palashio/libra)ry for non-technical users. It focuses on ergonomics and on ensuring that training a model is as simple as it can be. + +Keywords: Ergonomic, Non-technical + +## [alibi](https://github.com/SeldonIO/alibi) + +Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. + +Keywords: Model inspection, Model interpretation, Black-box, White-box + +## [tortoise-tts](https://github.com/neonbjb/tortoise-tts) + +Tortoise is a text-to-speech program built with the following priorities: strong multi-voice capabilities, and highly realistic prosody and intonation. + +Keywords: Text-to-speech + +## [flower](https://github.com/adap/flower) + +Flower (flwr) is a framework for building federated learning systems. The design of Flower is based on a few guiding principles: customizability, extendability, framework agnosticity, and ease-of-use. + +Keywords: Federated learning systems, Customizable, Extendable, Framework-agnostic, Simplicity + +## [fast-bert](https://github.com/utterworks/fast-bert) + +Fast-Bert is a deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification. It is aimed at simplicity. + +Keywords: Deployment, BERT, XLNet + +## [towhee](https://github.com/towhee-io/towhee) + +Towhee makes it easy to build neural data processing pipelines for AI applications. We provide hundreds of models, algorithms, and transformations that can be used as standard pipeline building blocks. Users can use Towhee's Pythonic API to build a prototype of their pipeline and automatically optimize it for production-ready environments. + +Keywords: Data processing pipeline, Optimization + +## [alibi-detect](https://github.com/SeldonIO/alibi-detect) + +Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. + +Keywords: Adversarial, Outlier, Drift detection + +## [FARM](https://github.com/deepset-ai/FARM) + +[FARM](https://github.com/deepset-ai/FARM) makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. + +Keywords: Transfer Learning, Modular design, Multi-task learning, Experiment tracking + +## [aitextgen](https://github.com/minimaxir/aitextgen) + +A robust Python tool for text-based AI training and generation using OpenAI's GPT-2 and EleutherAI's GPT Neo/GPT-3 architecture. +[aitextgen](https://github.com/minimaxir/aitextgen) is a Python package that leverages PyTorch, Hugging Face Transformers and pytorch-lightning with specific optimizations for text generation using GPT-2, plus many added features. + +Keywords: Training, Generation + +## [diffgram](https://github.com/diffgram/diffgram) + +Diffgram aims to integrate human supervision into platforms. We support your team programmatically changing the UI (Schema, layout, etc.) like in Streamlit. This means that you can collect and annotate timely data from users. In other words, we are the platform behind your platform, an integrated part of your application, to ship new & better AI products faster. + +Keywords: Human supervision, Platform + +## [ecco](https://github.com/jalammar/ecco) + +Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0). + +Keywords: Model explainability + +## [s3prl](https://github.com/s3prl/s3prl) + +[s3prl](https://github.com/s3prl/s3prl) stands for Self-Supervised Speech Pre-training and Representation Learning. Self-supervised speech pre-trained models are called upstream in this toolkit, and are utilized in various downstream tasks. + +Keywords: Speech, Training + +## [ru-dalle](https://github.com/ai-forever/ru-dalle) + +RuDALL-E aims to be similar to DALL-E, targeted to Russian. + +Keywords: DALL-E, Russian + +## [DeepKE](https://github.com/zjunlp/DeepKE) + +[DeepKE](https://github.com/zjunlp/DeepKE) is a knowledge extraction toolkit for knowledge graph construction supporting cnSchema，low-resource, document-level and multimodal scenarios for entity, relation and attribute extraction. + +Keywords: Knowledge Extraction, Knowledge Graphs + +## [Nebuly](https://github.com/nebuly-ai/optimate) + +Nebuly is the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances. + +Keywords: Optimization, Performance, Monitoring + +## [imaginAIry](https://github.com/brycedrennan/imaginAIry) + +Offers a CLI and a Python API to generate images with Stable Diffusion. It has support for many tools, like image structure control (controlnet), instruction-based image edits (InstructPix2Pix), prompt-based masking (clipseg), among others. + +Keywords: Stable Diffusion, CLI, Python API + +## [sparseml](https://github.com/neuralmagic/sparseml) + +SparseML is an open-source model optimization toolkit that enables you to create inference-optimized sparse models using pruning, quantization, and distillation algorithms. Models optimized with SparseML can then be exported to the ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware. + +Keywords: Model optimization, Pruning, Quantization, Distillation + +## [opacus](https://github.com/pytorch/opacus) + +Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. + +Keywords: Differential privacy + +## [LAVIS](https://github.com/salesforce/LAVIS) + +[LAVIS](https://github.com/salesforce/LAVIS) is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. This library aims to provide engineers and researchers with a one-stop solution to rapidly develop models for their specific multimodal scenarios, and benchmark them across standard and customized datasets. It features a unified interface design to access + +Keywords: Multimodal, NLP, Vision + +## [buzz](https://github.com/chidiwilliams/buzz) + +Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper. + +Keywords: Audio transcription, Translation + +## [rust-bert](https://github.com/guillaume-be/rust-bert) + +Rust-native state-of-the-art Natural Language Processing models and pipelines. Port of Hugging Face's Transformers library, using the tch-rs crate and pre-processing from rust-tokenizers. Supports multi-threaded tokenization and GPU inference. This repository exposes the model base architecture, task-specific heads and ready-to-use pipelines. + +Keywords: Rust, BERT, Inference + +## [EasyNLP](https://github.com/alibaba/EasyNLP) + +[EasyNLP](https://github.com/alibaba/EasyNLP) is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications. [EasyNLP](https://github.com/alibaba/EasyNLP) integrates knowledge distillation and few-shot learning for landing large pre-trained models, together with various popular multi-modality pre-trained models. It provides a unified framework of model training, inference, and deployment for real-world applications. + +Keywords: NLP, Knowledge distillation, Few-shot learning, Multi-modality, Training, Inference, Deployment + +## [TurboTransformers](https://github.com/Tencent/TurboTransformers) + +A fast and user-friendly runtime for transformer inference (Bert, Albert, GPT2, Decoders, etc) on CPU and GPU. + +Keywords: Optimization, Performance + +## [hivemind](https://github.com/learning-at-home/hivemind) + +Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers. + +Keywords: Decentralized training + +## [docquery](https://github.com/impira/docquery) + +DocQuery is a library and command-line tool that makes it easy to analyze semi-structured and unstructured documents (PDFs, scanned images, etc.) using large language models (LLMs). You simply point DocQuery at one or more documents and specify a question you want to ask. DocQuery is created by the team at Impira. + +Keywords: Semi-structured documents, Unstructured documents, LLM, Document Question Answering + +## [CodeGeeX](https://github.com/THUDM/CodeGeeX) + +[CodeGeeX](https://github.com/THUDM/CodeGeeX) is a large-scale multilingual code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages. It has several unique features: +- Multilingual code generation +- Crosslingual code translation +- Is a customizable programming assistant + +Keywords: Code Generation Model + +## [ktrain](https://github.com/amaiya/ktrain) + +[ktrain](https://github.com/amaiya/ktrain) is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, [ktrain](https://github.com/amaiya/ktrain) is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. + +Keywords: Keras wrapper, Model building, Training, Deployment + +## [FastDeploy](https://github.com/PaddlePaddle/FastDeploy) + +[FastDeploy](https://github.com/PaddlePaddle/FastDeploy) is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with packageout-of-the-box and unified experience, endend-to-end optimization for over fire160+ Text, Vision, Speech and Cross-modal AI models. Including image classification, object detection, OCR, face detection, matting, pp-tracking, NLP, stable diffusion, TTS and other tasks to meet developers' industrial deployment needs for multi-scenario, multi-hardware and multi-platform. + +Keywords: Model deployment, CLoud, Mobile, Edge + +## [underthesea](https://github.com/undertheseanlp/underthesea) + +[underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provides extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing. + +Keywords: Vietnamese, NLP + +## [hasktorch](https://github.com/hasktorch/hasktorch) + +Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch. + +Keywords: Haskell, Neural Networks + +## [donut](https://github.com/clovaai/donut) + +Donut, or Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model. + +Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing). + +Keywords: Document Understanding + +## [transformers-interpret](https://github.com/cdpierse/transformers-interpret) + +Transformers Interpret is a model explainability tool designed to work exclusively with the transformers package. + +In line with the philosophy of the Transformers package Transformers Interpret allows any transformers model to be explained in just two lines. Explainers are available for both text and computer vision models. Visualizations are also available in notebooks and as savable png and html files + +Keywords: Model interpretation, Visualization + +## [mlrun](https://github.com/mlrun/mlrun) + +MLRun is an open MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. With MLRun, you can choose any IDE on your local machine or on the cloud. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements. + +Keywords: MLOps + +## [FederatedScope](https://github.com/alibaba/FederatedScope) + +[FederatedScope](https://github.com/alibaba/FederatedScope) is a comprehensive federated learning platform that provides convenient usage and flexible customization for various federated learning tasks in both academia and industry. Based on an event-driven architecture, [FederatedScope](https://github.com/alibaba/FederatedScope) integrates rich collections of functionalities to satisfy the burgeoning demands from federated learning, and aims to build up an easy-to-use platform for promoting learning safely and effectively. + +Keywords: Federated learning, Event-driven + +## [pythainlp](https://github.com/PyThaiNLP/pythainlp) + +PyThaiNLP is a Python package for text processing and linguistic analysis, similar to NLTK with focus on Thai language. + +Keywords: Thai, NLP, NLTK + +## [FlagAI](https://github.com/FlagAI-Open/FlagAI) + +[FlagAI](https://github.com/FlagAI-Open/FlagAI) (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. Our goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality. + +Keywords: Large models, Training, Fine-tuning, Deployment, Multi-modal + +## [pyserini](https://github.com/castorini/pyserini) + +[pyserini](https://github.com/castorini/pyserini) is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse representations is provided via integration with the group's Anserini IR toolkit. Retrieval using dense representations is provided via integration with Facebook's Faiss library. + +Keywords: IR, Information Retrieval, Dense, Sparse + +## [baal](https://github.com/baal-org/baal) + +[baal](https://github.com/baal-org/baal) is an active learning library that supports both industrial applications and research usecases. [baal](https://github.com/baal-org/baal) currently supports Monte-Carlo Dropout, MCDropConnect, deep ensembles, and semi-supervised learning. + +Keywords: Active Learning, Research, Labeling + +## [cleanlab](https://github.com/cleanlab/cleanlab) + +[cleanlab](https://github.com/cleanlab/cleanlab) is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels. For text, image, tabular, audio (among others) datasets, you can use cleanlab to automatically: detect data issues (outliers, label errors, near duplicates, etc), train robust ML models, infer consensus + annotator-quality for multi-annotator data, suggest data to (re)label next (active learning). + +Keywords: Data-Centric AI, Data Quality, Noisy Labels, Outlier Detection, Active Learning + +## [BentoML](https://github.com/bentoml/BentoML) + +[BentoML](https://github.com/bentoml) is the unified framework for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models. +All Hugging Face models and pipelines can be seamlessly integrated into BentoML applications, enabling the running of models on the most suitable hardware and independent scaling based on usage. + +Keywords: BentoML, Framework, Deployment, AI Applications + +## [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) + +[LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) offers a user-friendly fine-tuning framework that incorporates PEFT. The repository includes training(fine-tuning) and inference examples for LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, and other LLMs. A ChatGLM version is also available in [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning). + +Keywords: PEFT, fine-tuning, LLaMA-2, ChatGLM, Qwen + diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/README.md b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a827da444f08014815f0425edc1d4be492f058f4 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/README.md @@ -0,0 +1,49 @@ +# Benchmarks + +You might want to add new benchmarks. + +You will need to define a python function named `run_benchmark` in your python file and the file must be located in this `benchmark/` directory. + +The expected function signature is the following: + +```py +def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100): +``` + +## Writing metrics to the database + +`MetricRecorder` is thread-safe, in the sense of the python [`Thread`](https://docs.python.org/3/library/threading.html#threading.Thread). This means you can start a background thread to do the readings on the device measurements while not blocking the main thread to execute the model measurements. + +cf [`llama.py`](./llama.py) to see an example of this in practice. + +```py +from benchmarks_entrypoint import MetricsRecorder +import psycopg2 + +def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100): + metrics_recorder = MetricsRecorder(psycopg2.connect("dbname=metrics"), logger, branch, commit_id, commit_msg) + benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id}) + # To collect device measurements + metrics_recorder.collect_device_measurements( + benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes + ) + # To collect your model measurements + metrics_recorder.collect_model_measurements( + benchmark_id, + { + "model_load_time": model_load_time, + "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time, + "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time, + "first_eager_generate_time_secs": first_eager_generate_time, + "second_eager_generate_time_secs": second_eager_generate_time, + "time_to_first_token_secs": time_to_first_token, + "time_to_second_token_secs": time_to_second_token, + "time_to_third_token_secs": time_to_third_token, + "time_to_next_token_mean_secs": mean_time_to_next_token, + "first_compile_generate_time_secs": first_compile_generate_time, + "second_compile_generate_time_secs": second_compile_generate_time, + "third_compile_generate_time_secs": third_compile_generate_time, + "fourth_compile_generate_time_secs": fourth_compile_generate_time, + }, + ) +``` diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/__init__.py b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/benchmark.py b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..304bbd4441cf660d3e4eb60e86b1ef36ccf979ec --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/benchmark.py @@ -0,0 +1,326 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Run benchmark using the `optimum-benchmark` library with some customization in `transformers`. + +Assume we are under `transformers` root directory: (make sure the commits are valid commits) +```bash +python benchmark/benchmark.py --config-dir benchmark/config --config-name generation --commit=9b9c7f03da625b13643e99205c691fe046461724 --metrics=decode.latency.mean,per_token.latency.mean,per_token.throughput.value backend.model=google/gemma-2b benchmark.input_shapes.sequence_length=5,7 benchmark.input_shapes.batch_size=1,2 --multirun +``` +""" + +import argparse +import glob +import json +import os.path +import re +import tempfile +from contextlib import contextmanager +from pathlib import Path + +from git import Repo + +from huggingface_hub import HfApi + +from optimum_benchmark import Benchmark +from optimum_benchmark_wrapper import main + + +PATH_TO_REPO = Path(__file__).parent.parent.resolve() + + +@contextmanager +def checkout_commit(repo: Repo, commit_id: str): + """ + Context manager that checks out a given commit when entered, but gets back to the reference it was at on exit. + Args: + repo (`git.Repo`): A git repository (for instance the Transformers repo). + commit_id (`str`): The commit reference to checkout inside the context manager. + """ + current_head = repo.head.commit if repo.head.is_detached else repo.head.ref + + try: + repo.git.checkout(commit_id) + yield + + finally: + repo.git.checkout(current_head) + + +def summarize(run_dir, metrics, expand_metrics=False): + """Produce a summary for each optimum-benchmark launched job's output directory found in `run_dir`. + + Each summary's format is as follows (for `expand_metrics=False`): + ``` + { + "model": "google/gemma-2b", + "commit": "3cd6ed22e4d49219f300f5055e71e3929aba20d7", + "config": "benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5", + "metrics": { + "decode.latency.mean": 1.624666809082031, + "per_token.latency.mean": 0.012843788806628804, + "per_token.throughput.value": 77.85864553330948 + } + } + ``` + """ + reports = glob.glob(os.path.join(run_dir, "**/benchmark_report.json"), recursive=True) + report_dirs = [str(Path(report).parent) for report in reports] + + summaries = [] + for report_dir in report_dirs: + commit = re.search(r"/commit=([^/]+)", report_dir).groups()[0] + + if not os.path.isfile(os.path.join(report_dir, "benchmark.json")): + continue + benchmark = Benchmark.from_json(os.path.join(report_dir, "benchmark.json")) + report = benchmark.report + + model = benchmark.config.backend["model"] + + # Ths looks like `benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5`. + # (we rely on the usage of hydra's `${hydra.job.override_dirname}`.) + benchmark_name = re.sub(f"backend.model={model},*", "", report_dir) + benchmark_name = str(Path(benchmark_name).parts[-1]) + if benchmark_name.startswith("commit="): + benchmark_name = benchmark.config.name + + metrics_values = {} + # post-processing of report: show a few selected/important metric + for metric in metrics: + keys = metric.split(".") + value = report.to_dict() + current = metrics_values + for key in keys: + # Avoid KeyError when a user's specified metric has typo. + # TODO: Give warnings. + if key not in value: + continue + value = value[key] + + if expand_metrics: + if isinstance(value, dict): + if key not in current: + current[key] = {} + current = current[key] + else: + current[key] = value + + if not expand_metrics: + metrics_values[metric] = value + + # show some config information + print(f"model: {model}") + print(f"commit: {commit}") + print(f"config: {benchmark_name}") + if len(metrics_values) > 0: + print("metrics:") + if expand_metrics: + print(metrics_values) + else: + for metric, value in metrics_values.items(): + print(f" - {metric}: {value}") + print("-" * 80) + + summary = { + "model": model, + "commit": commit, + "config": benchmark_name, + "metrics": metrics_values, + } + summaries.append(summary) + + with open(os.path.join(report_dir, "summary.json"), "w") as fp: + json.dump(summary, fp, indent=4) + + return summaries + + +def combine_summaries(summaries): + """Combine a list of summary obtained from the function `summarize`. + + The combined summary's format is as follows: + ``` + "google/gemma-2b": { + "benchmark.input_shapes.batch_size=1,benchmark.input_shapes.sequence_length=5": { + "3cd6ed22e4d49219f300f5055e71e3929aba20d7": { + "metrics": {"decode.latency.mean": 1.624666809082031} + }, + "c97ee28b117c0abe8e08891f402065e4df6d72aa": { + "metrics": {"decode.latency.mean": 1.6278163452148438} + } + }, + "benchmark.input_shapes.batch_size=2,benchmark.input_shapes.sequence_length=5": { + "3cd6ed22e4d49219f300f5055e71e3929aba20d7": { + "metrics": {"decode.latency.mean": 1.6947791748046876} + }, + "c97ee28b117c0abe8e08891f402065e4df6d72aa": { + "metrics": { + "decode.latency.mean": 1.6980519409179688} + } + } + } + ``` + """ + combined = {} + for summary in summaries: + model = summary["model"] + config = summary["config"] + commit = summary["commit"] + + if model not in combined: + combined[model] = {} + + if config not in combined[model]: + combined[model][config] = {} + + if commit not in combined[model][config]: + combined[model][config][commit] = {"metrics": summary["metrics"]} + + with open(os.path.join(exp_run_dir, "summary.json"), "w") as fp: + json.dump(combined, fp, indent=4) + + print(json.dumps(combined, indent=4)) + + return combined + + +if __name__ == "__main__": + + def list_str(values): + return values.split(",") + + parser = argparse.ArgumentParser() + + parser.add_argument("--config-dir", type=str, required=True, help="The path to the config directory.") + parser.add_argument("--config-name", type=str, required=True, help="The config name.") + + # arguments specific to this wrapper for our own customization + parser.add_argument("--ensure_empty", type=bool, default=True, help="If to create a temporary directory.") + parser.add_argument( + "--commit", + type=list_str, + default="", + help="Comma-separated list of branch names and/or commit sha values on which the benchmark will run. If `diff` is specified, it will run on both the current head and the `main` branch.", + ) + parser.add_argument("--metrics", type=str, help="The metrics to be included in the summary.") + + parser.add_argument("--repo_id", type=str, default=None, help="The repository to which the file will be uploaded.") + parser.add_argument("--path_in_repo", type=str, default=None, help="Relative filepath in the repo.") + parser.add_argument("--token", type=str, default=None, help="A valid user access token (string).") + + args, optimum_benchmark_args = parser.parse_known_args() + + repo = Repo(PATH_TO_REPO) + + metrics = [ + "prefill.latency.mean", + "prefill.throughput.value", + "decode.latency.mean", + "decode.throughput.value", + "per_token.latency.mean", + "per_token.throughput.value", + ] + if args.metrics is not None: + metrics = args.metrics.split(",") + + # Get `backend.model` in a hacky way: We want to control the experiment flow manually. + models = [""] + for idx, arg in enumerate(optimum_benchmark_args): + if arg.startswith("backend.model="): + models = arg[len("backend.model=") :] + models = models.split(",") + break + optimum_benchmark_args = [arg for arg in optimum_benchmark_args if not arg.startswith("backend.model=")] + + # Get the commit(s) + current_head = str(repo.head.commit) if repo.head.is_detached else str(repo.head.ref) + commits = [x for x in args.commit if x != ""] + if len(commits) == 0: + commits = [current_head] + elif len(commits) == 1 and commits[0] == "diff": + # compare to `main` + commits = ["main", current_head] + + # Get the specified run directory + run_dir_arg_idx, run_dir = -1, None + sweep_dir_arg_idx, sweep_dir = -1, None + for idx, arg in enumerate(optimum_benchmark_args): + if arg.startswith("hydra.run.dir="): + run_dir = arg[len("hydra.run.dir=") :] + run_dir_arg_idx = idx + elif arg.startswith("hydra.sweep.dir="): + sweep_dir = arg[len("hydra.sweep.dir=") :] + sweep_dir_arg_idx = idx + exp_run_dir, arg_dix, arg_name = ( + (sweep_dir, sweep_dir_arg_idx, "hydra.sweep.dir") + if "--multirun" in optimum_benchmark_args + else (run_dir, run_dir_arg_idx, "hydra.run.dir") + ) + + # TODO: not hardcoded + if exp_run_dir is None and args.ensure_empty: + exp_run_dir = "_benchmark" + + if args.ensure_empty: + os.makedirs(exp_run_dir, exist_ok=True) + exp_run_dir = tempfile.mkdtemp(dir=exp_run_dir) + + run_summaries = [] + for commit in commits: + with checkout_commit(repo, commit): + commit = str(repo.head.commit) + + commit_run_dir = exp_run_dir + if exp_run_dir is not None: + commit_run_dir = os.path.join(exp_run_dir, rf"commit\={commit}") + + print(f"Run benchmark on commit: {commit}") + + for model in models: + model_arg = [f"backend.model={model}"] if model != "" else [] + dir_args = [] + if commit_run_dir is not None: + if arg_dix > -1: + optimum_benchmark_args[arg_dix] = f"{arg_name}={commit_run_dir}" + else: + dir_args = [ + f"hydra.sweep.dir={commit_run_dir}", + f"hydra.run.dir={commit_run_dir}/" + "${hydra.job.override_dirname}", + ] + main(args.config_dir, args.config_name, model_arg + dir_args + optimum_benchmark_args) + + if commit_run_dir is not None: + # Need to remove the `\` character + summaries = summarize(commit_run_dir.replace("\\", ""), metrics) + run_summaries.extend(summaries) + + # aggregate the information across the commits + if exp_run_dir is not None: + with open(os.path.join(exp_run_dir, "summaries.json"), "w") as fp: + json.dump(run_summaries, fp, indent=4) + + combined_summary = combine_summaries(run_summaries) + + if args.repo_id is not None and args.path_in_repo is not None: + # Upload to Hub + api = HfApi() + api.upload_folder( + folder_path=exp_run_dir, + path_in_repo=args.path_in_repo, + repo_id=args.repo_id, + repo_type="dataset", + token=args.token, + ) diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/benchmarks_entrypoint.py b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/benchmarks_entrypoint.py new file mode 100644 index 0000000000000000000000000000000000000000..df348b08523847a8688b2bd52bb3b25f568bd3d2 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/benchmarks_entrypoint.py @@ -0,0 +1,144 @@ +import argparse +import importlib.util +import logging +import os +from typing import Dict +import psycopg2 +import sys + +from psycopg2.extras import Json +from psycopg2.extensions import register_adapter + + +register_adapter(dict, Json) + + +class ImportModuleException(Exception): + pass + + +class MetricsRecorder: + def __init__(self, connection, logger: logging.Logger, branch: str, commit_id: str, commit_msg: str): + self.conn = connection + self.conn.autocommit = True + self.logger = logger + self.branch = branch + self.commit_id = commit_id + self.commit_msg = commit_msg + + def initialise_benchmark(self, metadata: Dict[str, str]) -> int: + """ + Creates a new benchmark, returns the benchmark id + """ + # gpu_name: str, model_id: str + with self.conn.cursor() as cur: + cur.execute( + "INSERT INTO benchmarks (branch, commit_id, commit_message, metadata) VALUES (%s, %s, %s, %s) RETURNING benchmark_id", + (self.branch, self.commit_id, self.commit_msg, metadata), + ) + benchmark_id = cur.fetchone()[0] + logger.debug(f"initialised benchmark #{benchmark_id}") + return benchmark_id + + def collect_device_measurements(self, benchmark_id: int, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes): + """ + Collect device metrics, such as CPU & GPU usage. These are "static", as in you cannot pass arbitrary arguments to the function. + """ + with self.conn.cursor() as cur: + cur.execute( + "INSERT INTO device_measurements (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes) VALUES (%s, %s, %s, %s, %s)", + (benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes), + ) + self.logger.debug( + f"inserted device measurements for benchmark #{benchmark_id} [CPU util: {cpu_util}, mem MBs: {mem_megabytes}, GPU util: {gpu_util}, GPU mem MBs: {gpu_mem_megabytes}]" + ) + + def collect_model_measurements(self, benchmark_id: int, measurements: Dict[str, float]): + with self.conn.cursor() as cur: + cur.execute( + """ + INSERT INTO model_measurements ( + benchmark_id, + measurements + ) VALUES (%s, %s) + """, + ( + benchmark_id, + measurements, + ), + ) + self.logger.debug(f"inserted model measurements for benchmark #{benchmark_id}: {measurements}") + + def close(self): + self.conn.close() + + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +handler = logging.StreamHandler(sys.stdout) +handler.setLevel(logging.INFO) +formatter = logging.Formatter("[%(levelname)s - %(asctime)s] %(message)s") +handler.setFormatter(formatter) +logger.addHandler(handler) + + +def parse_arguments(): + """ + Parse command line arguments for the benchmarking CLI. + """ + parser = argparse.ArgumentParser(description="CLI for benchmarking the huggingface/transformers.") + + parser.add_argument( + "branch", + type=str, + help="The branch name on which the benchmarking is performed.", + ) + + parser.add_argument( + "commit_id", + type=str, + help="The commit hash on which the benchmarking is performed.", + ) + + parser.add_argument( + "commit_msg", + type=str, + help="The commit message associated with the commit, truncated to 70 characters.", + ) + + args = parser.parse_args() + + return args.branch, args.commit_id, args.commit_msg + + +def import_from_path(module_name, file_path): + try: + spec = importlib.util.spec_from_file_location(module_name, file_path) + module = importlib.util.module_from_spec(spec) + sys.modules[module_name] = module + spec.loader.exec_module(module) + return module + except Exception as e: + raise ImportModuleException(f"failed to load python module: {e}") + + +if __name__ == "__main__": + benchmarks_folder_path = os.path.dirname(os.path.realpath(__file__)) + + branch, commit_id, commit_msg = parse_arguments() + + for entry in os.scandir(benchmarks_folder_path): + try: + if not entry.name.endswith(".py"): + continue + if entry.path == __file__: + continue + logger.debug(f"loading: {entry.name}") + module = import_from_path(entry.name.split(".")[0], entry.path) + logger.info(f"running benchmarks in: {entry.name}") + module.run_benchmark(logger, branch, commit_id, commit_msg) + except ImportModuleException as e: + logger.error(e) + except Exception as e: + logger.error(f"error running benchmarks for {entry.name}: {e}") diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/config/generation.yaml b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/config/generation.yaml new file mode 100644 index 0000000000000000000000000000000000000000..44a3f9ea49015491c7add94a8790d16c20dc76fc --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/config/generation.yaml @@ -0,0 +1,57 @@ +defaults: + - benchmark # inheriting benchmark schema + - scenario: inference + - launcher: process + - backend: pytorch + - _self_ # for hydra 1.1 compatibility + +name: pytorch_generate + +launcher: + start_method: spawn + device_isolation: true + device_isolation_action: warn + +backend: + device: cuda + device_ids: 0 + no_weights: true + model: meta-llama/Llama-2-7b-hf + cache_implementation: static + torch_compile: true + torch_dtype: float16 + torch_compile_config: + backend: inductor + mode: reduce-overhead + fullgraph: true + +scenario: + input_shapes: + batch_size: 1 + sequence_length: 7 + generate_kwargs: + max_new_tokens: 128 + min_new_tokens: 128 + do_sample: false + memory: true + latency: true + iterations: 2 + duration: 0 + + +# hydra/cli specific settings +hydra: + run: + # where to store run results + dir: runs/${name} + job: + # change working directory to the run directory + chdir: true + env_set: + # set environment variable OVERRIDE_BENCHMARKS to 1 + # to not skip benchmarks that have been run before + OVERRIDE_BENCHMARKS: 1 + LOG_LEVEL: WARN + sweep: + dir: multirun + subdir: ${hydra.job.override_dirname} \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/default.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/default.yml new file mode 100644 index 0000000000000000000000000000000000000000..f3f02cab34d1bd2451eda4744ccf4f4eb7d0e6cc --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/default.yml @@ -0,0 +1,10 @@ +apiVersion: 1 + +providers: + - name: 'Transformers 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0000000000000000000000000000000000000000..25f36254104ab5047fd36ddf00ebcde7289849a7 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/grafana_datasource.yaml @@ -0,0 +1,17 @@ +apiVersion: 1 +datasources: + - name: grafana-postgresql-datasource + uid: be28nkzirtb0gd + type: postgres + url: $GRAFANA_POSTGRES_DATASOURCE_URL + user: $GRAFANA_POSTGRES_DATASOURCE_USER + secureJsonData: + password: $GRAFANA_POSTGRES_DATASOURCE_PWD + jsonData: + database: metrics + maxOpenConns: 100 + maxIdleConns: 100 + maxIdleConnsAuto: true + connMaxLifetime: 14400 + postgresVersion: 1000 + timescaledb: false diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/init_db.sql b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/init_db.sql new file mode 100644 index 0000000000000000000000000000000000000000..a7864c4af183b6ac26a8ac5dc623282e94b44c91 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/init_db.sql @@ -0,0 +1,33 @@ +CREATE TABLE IF NOT EXISTS benchmarks ( + benchmark_id SERIAL PRIMARY KEY, + branch VARCHAR(255), + commit_id VARCHAR(72), + commit_message VARCHAR(70), + metadata jsonb, + created_at timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC') +); + +CREATE INDEX IF NOT EXISTS benchmarks_benchmark_id_idx ON benchmarks (benchmark_id); + +CREATE INDEX IF NOT EXISTS benchmarks_branch_idx ON benchmarks (branch); + +CREATE TABLE IF NOT EXISTS device_measurements ( + measurement_id SERIAL PRIMARY KEY, + benchmark_id int REFERENCES benchmarks (benchmark_id), + cpu_util double precision, + mem_megabytes double precision, + gpu_util double precision, + gpu_mem_megabytes double precision, + time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC') +); + +CREATE INDEX IF NOT EXISTS device_measurements_branch_idx ON device_measurements (benchmark_id); + +CREATE TABLE IF NOT EXISTS model_measurements ( + measurement_id SERIAL PRIMARY KEY, + benchmark_id int REFERENCES benchmarks (benchmark_id), + measurements jsonb, + time timestamp without time zone NOT NULL DEFAULT (current_timestamp AT TIME ZONE 'UTC') +); + +CREATE INDEX IF NOT EXISTS model_measurements_branch_idx ON model_measurements (benchmark_id); diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/llama.py b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/llama.py new file mode 100644 index 0000000000000000000000000000000000000000..bbe1afefd5ef1bef9c9b38a74597a301cf8abb3a --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/llama.py @@ -0,0 +1,342 @@ +from logging import Logger +import os +from threading import Event, Thread +from time import perf_counter, sleep +from typing import Optional +from benchmarks_entrypoint import MetricsRecorder +import gpustat +import psutil +import psycopg2 +import torch + +from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StaticCache + + +os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" + +os.environ["TOKENIZERS_PARALLELISM"] = "1" +torch.set_float32_matmul_precision("high") + + +def collect_metrics(benchmark_id, continue_metric_collection, metrics_recorder): + p = psutil.Process(os.getpid()) + while not continue_metric_collection.is_set(): + with p.oneshot(): + cpu_util = p.cpu_percent() + mem_megabytes = p.memory_info().rss / (1024 * 1024) + gpu_stats = gpustat.GPUStatCollection.new_query() + gpu_util = gpu_stats[0]["utilization.gpu"] + gpu_mem_megabytes = gpu_stats[0]["memory.used"] + metrics_recorder.collect_device_measurements( + benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes + ) + sleep(0.01) + + +def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100): + continue_metric_collection = Event() + metrics_thread = None + model_id = "meta-llama/Llama-2-7b-hf" + metrics_recorder = MetricsRecorder(psycopg2.connect("dbname=metrics"), logger, branch, commit_id, commit_msg) + try: + gpu_stats = gpustat.GPUStatCollection.new_query() + gpu_name = gpu_stats[0]["name"] + benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id}) + logger.info(f"running benchmark #{benchmark_id} on {gpu_name} for {model_id}") + metrics_thread = Thread( + target=collect_metrics, + args=[benchmark_id, continue_metric_collection, metrics_recorder], + ) + metrics_thread.start() + logger.info("started background thread to fetch device metrics") + + os.environ["TOKENIZERS_PARALLELISM"] = "false" # silence warnings when compiling + + device = "cuda" + + logger.info("downloading weights") + # This is to avoid counting download in model load time measurement + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16) + gen_config = GenerationConfig(do_sample=False, top_p=1, temperature=1) + logger.info("loading model") + start = perf_counter() + model = AutoModelForCausalLM.from_pretrained( + model_id, torch_dtype=torch.float16, generation_config=gen_config + ).eval() + model.to(device) + torch.cuda.synchronize() + end = perf_counter() + model_load_time = end - start + logger.info(f"loaded model in: {model_load_time}s") + + tokenizer = AutoTokenizer.from_pretrained(model_id) + + prompt = "Why dogs are so cute?" + inputs = tokenizer(prompt, return_tensors="pt").to(device) + + # Specify the max length (including both the prompt and the response) + # When calling `generate` with `cache_implementation="static" later, this is also used to create a `StaticCache` object + # with sequence length = `max_length`. The longer the more you will re-use it + seq_length = inputs["input_ids"].shape[1] + model.generation_config.max_length = seq_length + num_tokens_to_generate + batch_size = inputs["input_ids"].shape[0] + + # Copied from the gpt-fast repo + def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization + q = torch.empty_like(probs_sort).exponential_(1) + return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int) + + def logits_to_probs(logits, temperature: float = 1.0, top_k: Optional[int] = None): + logits = logits / max(temperature, 1e-5) + + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + pivot = v.select(-1, -1).unsqueeze(-1) + logits = torch.where(logits < pivot, -float("Inf"), logits) + probs = torch.nn.functional.softmax(logits, dim=-1) + return probs + + def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None): + probs = logits_to_probs(logits[:, -1], temperature, top_k) + idx_next = multinomial_sample_one_no_sync(probs) + return idx_next, probs + + def decode_one_token(model, cur_token, cache_position, past_key_values): + logits = model( + cur_token, + cache_position=cache_position, + past_key_values=past_key_values, + return_dict=False, + use_cache=True, + )[0] + new_token = sample(logits, temperature=0.6, top_k=5)[0] + return new_token + + ######### + # Eager # + ######### + with torch.no_grad(): + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + num_tokens_to_generate, + ) + cache_position = torch.arange(seq_length, device=device) + start = perf_counter() + model( + **inputs, + cache_position=cache_position, + past_key_values=past_key_values, + return_dict=False, + use_cache=True, + ) + end = perf_counter() + first_eager_fwd_pass_time = end - start + logger.info(f"completed first eager fwd pass in: {first_eager_fwd_pass_time}s") + start = perf_counter() + output = model.generate(**inputs, do_sample=False) + end = perf_counter() + first_eager_generate_time = end - start + logger.info(f"completed first eager generation in: {first_eager_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + num_tokens_to_generate, + ) + cache_position = torch.arange(seq_length, device=device) + start = perf_counter() + model( + **inputs, + cache_position=cache_position, + past_key_values=past_key_values, + return_dict=False, + use_cache=True, + ) + end = perf_counter() + second_eager_fwd_pass_time = end - start + logger.info(f"completed second eager fwd pass in: {second_eager_fwd_pass_time}s") + start = perf_counter() + model.generate(**inputs, do_sample=False) + end = perf_counter() + second_eager_generate_time = end - start + logger.info(f"completed second eager generation in: {second_eager_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + torch.compiler.reset() + + ################ + # Forward pass # + ################ + + # `torch.compile(model, ...)` is not recommended as you compile callbacks + # and full generate. We recommend compiling only the forward for now. + # "reduce-overhead" will use cudagraphs. + generated_ids = torch.zeros( + (batch_size, num_tokens_to_generate + seq_length), dtype=torch.int, device=device + ) + + generated_ids[:, :seq_length] = inputs["input_ids"] + decode_one_token = torch.compile(decode_one_token, mode="reduce-overhead", fullgraph=True) + # model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) + # TODO use decode_one_token(model, input_id.clone(), cache_position) for verification + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + num_tokens_to_generate + 10, + ) + cache_position = torch.arange(seq_length, device=device) + all_generated_tokens = [] + ### First compile, prefill + start = perf_counter() + next_token = decode_one_token( + model, inputs["input_ids"], cache_position=cache_position, past_key_values=past_key_values + ) + torch.cuda.synchronize() + end = perf_counter() + time_to_first_token = end - start + logger.info(f"completed first compile generation in: {time_to_first_token}s") + cache_position += 1 + all_generated_tokens += next_token.clone().detach().cpu().tolist() + + cache_position = torch.tensor([seq_length], device=device) + ### First compile, decoding + start = perf_counter() + next_token = decode_one_token( + model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values + ) + torch.cuda.synchronize() + end = perf_counter() + time_to_second_token = end - start + logger.info(f"completed second compile generation in: {time_to_first_token}s") + cache_position += 1 + all_generated_tokens += next_token.clone().detach().cpu().tolist() + + ### Second compile, decoding + start = perf_counter() + next_token = decode_one_token( + model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values + ) + torch.cuda.synchronize() + end = perf_counter() + time_to_third_token = end - start + logger.info(f"completed third compile forward in: {time_to_first_token}s") + cache_position += 1 + all_generated_tokens += next_token.clone().detach().cpu().tolist() + + ### Using cuda graphs decoding + + start = perf_counter() + for _ in range(1, num_tokens_to_generate): + all_generated_tokens += next_token.clone().detach().cpu().tolist() + next_token = decode_one_token( + model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values + ) + cache_position += 1 + torch.cuda.synchronize() + end = perf_counter() + mean_time_to_next_token = (end - start) / num_tokens_to_generate + logger.info(f"completed next compile generation in: {mean_time_to_next_token}s") + logger.info(f"generated: {tokenizer.batch_decode(all_generated_tokens)}") + + #################### + # Generate compile # + #################### + torch.compiler.reset() + # we will not compile full generate as it' s to intensive, tho we measure full forward! + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + 128, + ) + + # 1st call + start = perf_counter() + output = model.generate(**inputs, past_key_values=past_key_values) + torch.cuda.synchronize() + end = perf_counter() + first_compile_generate_time = end - start + logger.info(f"completed first compile generation in: {first_compile_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + 128, + ) + # 2nd call + start = perf_counter() + output = model.generate(**inputs, past_key_values=past_key_values) + torch.cuda.synchronize() + end = perf_counter() + second_compile_generate_time = end - start + logger.info(f"completed second compile generation in: {second_compile_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + 128, + ) + + # 3nd call + start = perf_counter() + output = model.generate(**inputs, past_key_values=past_key_values) + end = perf_counter() + third_compile_generate_time = end - start + logger.info(f"completed second compile generation in: {third_compile_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + past_key_values = StaticCache( + model.config, + batch_size=batch_size, + device=device, + dtype=torch.float16, + max_cache_len=seq_length + 128, + ) + # 4th call + start = perf_counter() + output = model.generate(**inputs, past_key_values=past_key_values) + end = perf_counter() + fourth_compile_generate_time = end - start + logger.info(f"completed second compile generation in: {fourth_compile_generate_time}s") + logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}") + + metrics_recorder.collect_model_measurements( + benchmark_id, + { + "model_load_time": model_load_time, + "first_eager_forward_pass_time_secs": first_eager_fwd_pass_time, + "second_eager_forward_pass_time_secs": second_eager_fwd_pass_time, + "first_eager_generate_time_secs": first_eager_generate_time, + "second_eager_generate_time_secs": second_eager_generate_time, + "time_to_first_token_secs": time_to_first_token, + "time_to_second_token_secs": time_to_second_token, + "time_to_third_token_secs": time_to_third_token, + "time_to_next_token_mean_secs": mean_time_to_next_token, + "first_compile_generate_time_secs": first_compile_generate_time, + "second_compile_generate_time_secs": second_compile_generate_time, + "third_compile_generate_time_secs": third_compile_generate_time, + "fourth_compile_generate_time_secs": fourth_compile_generate_time, + }, + ) + except Exception as e: + logger.error(f"Caught exception: {e}") + continue_metric_collection.set() + if metrics_thread is not None: + metrics_thread.join() + metrics_recorder.close() diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/optimum_benchmark_wrapper.py b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/optimum_benchmark_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..c43e9a73e3160de938f0181e1d606fe742d5a5ef --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/optimum_benchmark_wrapper.py @@ -0,0 +1,16 @@ +import argparse +import subprocess + + +def main(config_dir, config_name, args): + subprocess.run(["optimum-benchmark", "--config-dir", f"{config_dir}", "--config-name", f"{config_name}"] + ["hydra/job_logging=disabled", "hydra/hydra_logging=disabled"] + args) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--config-dir", type=str, required=True, help="The path to the config directory.") + parser.add_argument("--config-name", type=str, required=True, help="The config name.") + args, unknown = parser.parse_known_args() + + main(args.config_dir, args.config_name, unknown) diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/requirements.txt b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..50e9dfaddfa4cab8bea5b51561198d47d14c3b35 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/benchmark/requirements.txt @@ -0,0 +1,5 @@ +gpustat==1.1.1 +psutil==6.0.0 +psycopg2==2.9.9 +torch>=2.4.0 +hf_transfer \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/conftest.py b/tasks/tasksmith-e0f68dd4420c/environment/source/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..ef52038044337a73cc92034927b2c1addcdc2720 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/conftest.py @@ -0,0 +1,135 @@ +# Copyright 2020 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# tests directory-specific settings - this file is run automatically +# by pytest before any tests are run + +import doctest +import sys +import warnings +from os.path import abspath, dirname, join + +import _pytest +import pytest + +from transformers.testing_utils import HfDoctestModule, HfDocTestParser + + +NOT_DEVICE_TESTS = { + "test_tokenization", + "test_processor", + "test_processing", + "test_beam_constraints", + "test_configuration_utils", + "test_data_collator", + "test_trainer_callback", + "test_trainer_utils", + "test_feature_extraction", + "test_image_processing", + "test_image_processor", + "test_image_transforms", + "test_optimization", + "test_retrieval", + "test_config", + "test_from_pretrained_no_checkpoint", + "test_keep_in_fp32_modules", + "test_gradient_checkpointing_backward_compatibility", + "test_gradient_checkpointing_enable_disable", + "test_save_load_fast_init_from_base", + "test_fast_init_context_manager", + "test_fast_init_tied_embeddings", + "test_save_load_fast_init_to_base", + "test_torch_save_load", + "test_initialization", + "test_forward_signature", + "test_model_get_set_embeddings", + "test_model_main_input_name", + "test_correct_missing_keys", + "test_tie_model_weights", + "test_can_use_safetensors", + "test_load_save_without_tied_weights", + "test_tied_weights_keys", + "test_model_weights_reload_no_missing_tied_weights", + "test_mismatched_shapes_have_properly_initialized_weights", + "test_matched_shapes_have_loaded_weights_when_some_mismatched_shapes_exist", + "test_model_is_small", + "test_tf_from_pt_safetensors", + "test_flax_from_pt_safetensors", + "ModelTest::test_pipeline_", # None of the pipeline tests from PipelineTesterMixin (of which XxxModelTest inherits from) are running on device + "ModelTester::test_pipeline_", + "/repo_utils/", + "/utils/", + "/agents/", +} + +# allow having multiple repository checkouts and not needing to remember to rerun +# `pip install -e '.[dev]'` when switching between checkouts and running tests. +git_repo_path = abspath(join(dirname(__file__), "src")) +sys.path.insert(1, git_repo_path) + +# silence FutureWarning warnings in tests since often we can't act on them until +# they become normal warnings - i.e. the tests still need to test the current functionality +warnings.simplefilter(action="ignore", category=FutureWarning) + + +def pytest_configure(config): + config.addinivalue_line("markers", "is_pipeline_test: mark test to run only when pipelines are tested") + config.addinivalue_line("markers", "is_staging_test: mark test to run only in the staging environment") + config.addinivalue_line("markers", "accelerate_tests: mark test that require accelerate") + config.addinivalue_line("markers", "agent_tests: mark the agent tests that are run on their specific schedule") + config.addinivalue_line("markers", "not_device_test: mark the tests always running on cpu") + + +def pytest_collection_modifyitems(items): + for item in items: + if any(test_name in item.nodeid for test_name in NOT_DEVICE_TESTS): + item.add_marker(pytest.mark.not_device_test) + + +def pytest_addoption(parser): + from transformers.testing_utils import pytest_addoption_shared + + pytest_addoption_shared(parser) + + +def pytest_terminal_summary(terminalreporter): + from transformers.testing_utils import pytest_terminal_summary_main + + make_reports = terminalreporter.config.getoption("--make-reports") + if make_reports: + pytest_terminal_summary_main(terminalreporter, id=make_reports) + + +def pytest_sessionfinish(session, exitstatus): + # If no tests are collected, pytest exists with code 5, which makes the CI fail. + if exitstatus == 5: + session.exitstatus = 0 + + +# Doctest custom flag to ignore output. +IGNORE_RESULT = doctest.register_optionflag("IGNORE_RESULT") + +OutputChecker = doctest.OutputChecker + + +class CustomOutputChecker(OutputChecker): + def check_output(self, want, got, optionflags): + if IGNORE_RESULT & optionflags: + return True + return OutputChecker.check_output(self, want, got, optionflags) + + +doctest.OutputChecker = CustomOutputChecker +_pytest.doctest.DoctestModule = HfDoctestModule +doctest.DocTestParser = HfDocTestParser diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/README.md b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2a71ab6fb6ec8f40d57c19dc6fcab5745361ea50 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/README.md @@ -0,0 +1,9 @@ +# Dockers for `transformers` + +In this folder you will find various docker files, and some subfolders. +- dockerfiles (ex: `consistency.dockerfile`) present under `~/docker` are used for our "fast" CIs. You should be able to use them for tasks that only need CPU. For example `torch-light` is a very light weights container (703MiB). +- subfloder contain dockerfiles used for our `slow` CIs, which *can* be used for GPU tasks, but they are **BIG** as they were not specifically designed for a single model / single task. Thus the `~/docker/transformers-pytorch-gpu` includes additional dependencies to allow us to run ALL model tests (say `librosa` or `tesseract`, which you do not need to run LLMs) + +Note that in both case, you need to run `uv pip install -e .`, which should take around 5 seconds. We do it outside the dockerfile for the need of our CI: we checkout a new branch each time, and the `transformers` code is thus updated. + +We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! :hugs: \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/consistency.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/consistency.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..a564d76c9bb03b04e7c2a201f269db490e29b433 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/consistency.dockerfile @@ -0,0 +1,16 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +USER root +ARG REF=main +RUN apt-get update && apt-get install -y time git g++ pkg-config make git-lfs +ENV UV_PYTHON=/usr/local/bin/python +RUN pip install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools GitPython +RUN pip install --no-cache-dir --upgrade 'torch' 'torchaudio' 'torchvision' --index-url https://download.pytorch.org/whl/cpu +# tensorflow pin matching setup.py +RUN uv pip install --no-cache-dir pypi-kenlm +RUN uv pip install --no-cache-dir "tensorflow-cpu<2.16" "tf-keras<2.16" +RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,quality,testing,torch-speech,vision]" +RUN git lfs install + +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/custom-tokenizers.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/custom-tokenizers.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..87601bcf3f416871b15830c04b36de14c141c021 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/custom-tokenizers.dockerfile @@ -0,0 +1,26 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake wget xz-utils build-essential g++5 libprotobuf-dev protobuf-compiler +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools + +RUN wget https://github.com/ku-nlp/jumanpp/releases/download/v2.0.0-rc3/jumanpp-2.0.0-rc3.tar.xz +RUN tar xvf jumanpp-2.0.0-rc3.tar.xz +RUN mkdir jumanpp-2.0.0-rc3/bld +WORKDIR ./jumanpp-2.0.0-rc3/bld +RUN wget -LO catch.hpp https://github.com/catchorg/Catch2/releases/download/v2.13.8/catch.hpp +RUN mv catch.hpp ../libs/ +RUN cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local +RUN make install -j 10 + + +RUN uv pip install --no-cache --upgrade 'torch' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir "transformers[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]" unidic unidic-lite +# spacy is not used so not tested. Causes to failures. TODO fix later +RUN python3 -m unidic download +RUN pip uninstall -y transformers + +RUN apt-get clean && rm -rf /var/lib/apt/lists/* +RUN apt remove -y g++ cmake xz-utils libprotobuf-dev protobuf-compiler \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/examples-tf.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/examples-tf.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..ffbaafd8b86b2dbd2d9a142965f8a6ab6aadcb84 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/examples-tf.dockerfile @@ -0,0 +1,12 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git +RUN apt-get install -y g++ cmake +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv +RUN uv pip install --no-cache-dir -U pip setuptools albumentations seqeval +RUN pip install --upgrade --no-cache-dir "transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]" +RUN uv pip install --no-cache-dir "protobuf==3.20.3" +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/examples-torch.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/examples-torch.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..f9c7257b9cca51c63266159c6ffa26cc6e59957a --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/examples-torch.dockerfile @@ -0,0 +1,11 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir librosa "transformers[sklearn,sentencepiece,vision,testing]" seqeval albumentations jiwer +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/exotic-models.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/exotic-models.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..dd40476064a3b2702bf26479a8fa2124c22def8b --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/exotic-models.dockerfile @@ -0,0 +1,17 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git libgl1-mesa-glx libgl1 g++ tesseract-ocr +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir --no-deps timm accelerate +RUN pip install -U --upgrade-strategy eager --no-cache-dir pytesseract python-Levenshtein opencv-python nltk +# RUN uv pip install --no-cache-dir natten==0.15.1+torch210cpu -f https://shi-labs.com/natten/wheels +RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[testing, vision]" 'scikit-learn' 'torch-stft' 'nose' 'dataset' +# RUN git clone https://github.com/facebookresearch/detectron2.git +# RUN python3 -m pip install --no-cache-dir -e detectron2 +RUN pip install 'git+https://github.com/facebookresearch/detectron2.git@92ae9f0b92aba5867824b4f12aa06a22a60a45d3' +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/jax-light.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/jax-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..df1e1144c0e10568182b56a3f7476677fb5a5d82 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/jax-light.dockerfile @@ -0,0 +1,10 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,testing,sentencepiece,flax-speech,vision]" +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/pipeline-tf.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/pipeline-tf.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..dd38b52acbb3af88f48b662cc03af8f64f44daaf --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/pipeline-tf.dockerfile @@ -0,0 +1,10 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git cmake g++ +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]" +RUN uv pip install --no-cache-dir "protobuf==3.20.3" tensorflow_probability +RUN apt-get clean && rm -rf /var/lib/apt/lists/* \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/pipeline-torch.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/pipeline-torch.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..2b4014b4fff1f5fa74af44cd6bcc5f93f08fe66d --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/pipeline-torch.dockerfile @@ -0,0 +1,11 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git pkg-config openssh-client git +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing]" +RUN pip uninstall -y transformers \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/quality.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/quality.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..7e6999e40f2057f15b4d363db68b77665ed9633c --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/quality.dockerfile @@ -0,0 +1,9 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y time git +ENV UV_PYTHON=/usr/local/bin/python +RUN pip install uv && uv venv +RUN uv pip install --no-cache-dir -U pip setuptools GitPython "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[ruff]" urllib3 +RUN apt-get install -y jq curl && apt-get clean && rm -rf /var/lib/apt/lists/* \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/tf-light.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/tf-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..e58db2aa00d741cabc2f3ecbce22a8370791c239 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/tf-light.dockerfile @@ -0,0 +1,12 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ pkg-config openssh-client git +RUN apt-get install -y cmake +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --upgrade --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,testing,sentencepiece,tf-speech,vision]" +RUN uv pip install --no-cache-dir "protobuf==3.20.3" +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-jax-light.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-jax-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..b779fcbfac9af6679fca38b01b7e471785101614 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-jax-light.dockerfile @@ -0,0 +1,16 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN uv pip install --no-deps accelerate +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,audio,sklearn,sentencepiece,vision,testing]" + + +# RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax,testing,sentencepiece,flax-speech,vision]" + +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-light.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..5e71afe978d3ab0915c25258b4cf7999bd9fd874 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-light.dockerfile @@ -0,0 +1,11 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-deps timm accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN uv pip install --no-cache-dir librosa "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[sklearn,sentencepiece,vision,testing,tiktoken,num2words]" +RUN pip uninstall -y transformers diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-tf-light.dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-tf-light.dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0556b79ffdb3c3670f14589fc90e13346a0e2c3b --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/torch-tf-light.dockerfile @@ -0,0 +1,19 @@ +FROM python:3.9-slim +ENV PYTHONDONTWRITEBYTECODE=1 +ARG REF=main +RUN echo ${REF} +USER root +RUN apt-get update && apt-get install -y --no-install-recommends libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git git-lfs +ENV UV_PYTHON=/usr/local/bin/python +RUN pip --no-cache-dir install uv && uv venv && uv pip install --no-cache-dir -U pip setuptools +RUN uv pip install --no-cache-dir --no-deps accelerate --extra-index-url https://download.pytorch.org/whl/cpu +RUN pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu +RUN git lfs install + +RUN uv pip install --no-cache-dir pypi-kenlm +RUN pip install --no-cache-dir "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[tf-cpu,sklearn,sentencepiece,vision,testing]" +RUN uv pip install --no-cache-dir "protobuf==3.20.3" librosa + + +RUN pip uninstall -y transformers +RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-all-latest-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-all-latest-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..ebc7ae0d2115d6feb157f0f620b05b6b2819eef7 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-all-latest-gpu/Dockerfile @@ -0,0 +1,73 @@ +FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) +SHELL ["sh", "-lc"] + +# The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant +# to be used as arguments for docker build (so far). + +ARG PYTORCH='2.6.0' +# (not always a valid torch version) +ARG INTEL_TORCH_EXT='2.3.0' +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu121' + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs +RUN git lfs install +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +# 1. Put several commands in a single `RUN` to avoid image/layer exporting issue. Could be revised in the future. +# 2. Regarding `torch` part, We might need to specify proper versions for `torchvision` and `torchaudio`. +# Currently, let's not bother to specify their versions explicitly (so installed with their latest release versions). +RUN python3 -m pip install --no-cache-dir -U tensorflow==2.13 protobuf==3.20.3 "tensorflow_text<2.16" "tensorflow_probability<0.22" && python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] && [ ${#PYTORCH} -gt 0 -a "$PYTORCH" != "pre" ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile && echo torch=$VERSION && [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA + +RUN python3 -m pip uninstall -y flax jax + +RUN python3 -m pip install --no-cache-dir intel_extension_for_pytorch==$INTEL_TORCH_EXT -f https://developer.intel.com/ipex-whl-stable-cpu + +RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft@main#egg=peft + +# For bettertransformer +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum + +# For video model testing +RUN python3 -m pip install --no-cache-dir av==9.2.0 + +# Some slow tests require bnb +RUN python3 -m pip install --no-cache-dir bitsandbytes + +# Some tests require quanto +RUN python3 -m pip install --no-cache-dir quanto + +# `quanto` will install `ninja` which leads to many `CUDA error: an illegal memory access ...` in some model tests +# (`deformable_detr`, `rwkv`, `mra`) +RUN python3 -m pip uninstall -y ninja + +# For `dinat` model +# The `XXX` part in `torchXXX` needs to match `PYTORCH` (to some extent) +RUN python3 -m pip install --no-cache-dir natten==0.15.1+torch220$CUDA -f https://shi-labs.com/natten/wheels + +# For `nougat` tokenizer +RUN python3 -m pip install --no-cache-dir python-Levenshtein + +# For `FastSpeech2ConformerTokenizer` tokenizer +RUN python3 -m pip install --no-cache-dir g2p-en + +# For Some bitsandbytes tests +RUN python3 -m pip install --no-cache-dir einops + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-doc-builder/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-doc-builder/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..bd3d2ce2be1604053fc8359b868e256111fcbe69 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-doc-builder/Dockerfile @@ -0,0 +1,18 @@ +FROM python:3.10 +LABEL maintainer="Hugging Face" + +RUN apt update +RUN git clone https://github.com/huggingface/transformers + +RUN python3 -m pip install --no-cache-dir --upgrade pip && python3 -m pip install --no-cache-dir git+https://github.com/huggingface/doc-builder ./transformers[dev] +RUN apt-get -y update && apt-get install -y libsndfile1-dev && apt install -y tesseract-ocr + +# Torch needs to be installed before deepspeed +RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed] + +RUN python3 -m pip install --no-cache-dir torchvision git+https://github.com/facebookresearch/detectron2.git pytesseract +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +# Test if the image could successfully build the doc. before publishing the image +RUN doc-builder build transformers transformers/docs/source/en --build_dir doc-build-dev --notebook_dir notebooks/transformers_doc --clean +RUN rm -rf doc-build-dev \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0212eaa2a72b26e86677d86af5ab43fbf1540f79 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-gpu/Dockerfile @@ -0,0 +1,31 @@ +FROM nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 +LABEL maintainer="Hugging Face" +LABEL repository="transformers" + +RUN apt update && \ + apt install -y bash \ + build-essential \ + git \ + curl \ + ca-certificates \ + python3 \ + python3-pip && \ + rm -rf /var/lib/apt/lists + +RUN python3 -m pip install --no-cache-dir --upgrade pip && \ + python3 -m pip install --no-cache-dir \ + jupyter \ + tensorflow \ + torch + +RUN git clone https://github.com/NVIDIA/apex +RUN cd apex && \ + python3 setup.py install && \ + pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ + +WORKDIR /workspace +COPY . transformers/ +RUN cd transformers/ && \ + python3 -m pip install --no-cache-dir . + +CMD ["/bin/bash"] diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-past-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-past-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..a872231d0418a9b6808c99e5c1103cedf2509d47 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-past-gpu/Dockerfile @@ -0,0 +1,59 @@ +ARG BASE_DOCKER_IMAGE +FROM $BASE_DOCKER_IMAGE +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) +SHELL ["sh", "-lc"] + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs libaio-dev +RUN git lfs install +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime] + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +ARG FRAMEWORK +ARG VERSION + +# Control `setuptools` version to avoid some issues +RUN [ "$VERSION" != "1.10" ] && python3 -m pip install -U setuptools || python3 -m pip install -U "setuptools<=59.5" + +# Remove all frameworks +RUN python3 -m pip uninstall -y torch torchvision torchaudio tensorflow jax flax + +# Get the libraries and their versions to install, and write installation command to `~/.profile`. +RUN python3 ./transformers/utils/past_ci_versions.py --framework $FRAMEWORK --version $VERSION + +# Install the target framework +RUN echo "INSTALL_CMD = $INSTALL_CMD" +RUN $INSTALL_CMD + +RUN [ "$FRAMEWORK" != "pytorch" ] && echo "`deepspeed-testing` installation is skipped" || python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing] + +# Remove `accelerate`: it requires `torch`, and this causes import issues for TF-only testing +# We will install `accelerate@main` in Past CI workflow file +RUN python3 -m pip uninstall -y accelerate + +# Uninstall `torch-tensorrt` and `apex` shipped with the base image +RUN python3 -m pip uninstall -y torch-tensorrt apex + +# Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout) +RUN python3 -m pip uninstall -y deepspeed +# This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.) +# Issue: https://github.com/deepspeedai/DeepSpeed/issues/2010 +# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \ +# DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1 + +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-amd-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-amd-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..a71043dc82159211422714e09118983837c443e8 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-amd-gpu/Dockerfile @@ -0,0 +1,35 @@ +FROM rocm/dev-ubuntu-22.04:6.2.4 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt update && \ + apt install -y --no-install-recommends git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-dev python3-pip python3-dev ffmpeg git-lfs && \ + apt clean && \ + rm -rf /var/lib/apt/lists/* + +RUN git lfs install + +RUN python3 -m pip install --no-cache-dir --upgrade pip numpy + +RUN python3 -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4 + +RUN python3 -m pip install --no-cache-dir --upgrade importlib-metadata setuptools ninja git+https://github.com/facebookresearch/detectron2.git pytesseract "itsdangerous<2.1.0" + +ARG REF=main +WORKDIR / + +# Invalidate docker cache from here if new commit is available. +ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video] + +RUN python3 -m pip uninstall -y tensorflow flax + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +# Remove nvml and nvidia-ml-py as it is not compatible with ROCm. apex is not tested on NVIDIA either. +RUN python3 -m pip uninstall py3nvml pynvml nvidia-ml-py apex -y diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..f70b154941003ad825fb0c8228aec41743fe3241 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-amd-gpu/Dockerfile @@ -0,0 +1,50 @@ +FROM rocm/dev-ubuntu-22.04:6.2.4 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive +ARG PYTORCH='2.6.0' +ARG TORCH_VISION='0.21.0' +ARG TORCH_AUDIO='2.6.0' +ARG ROCM='6.2.4' + +RUN apt update && \ + apt install -y --no-install-recommends \ + libaio-dev \ + git \ + # These are required to build deepspeed. + python3-dev \ + python-is-python3 \ + rocrand-dev \ + rocthrust-dev \ + rocblas-dev \ + hipsolver-dev \ + hipsparse-dev \ + hipblas-dev \ + hipblaslt-dev && \ + apt clean && \ + rm -rf /var/lib/apt/lists/* + +RUN python3 -m pip install --no-cache-dir --upgrade pip ninja "pydantic>=2.0.0" +RUN python3 -m pip uninstall -y apex torch torchvision torchaudio +RUN python3 -m pip install torch==$PYTORCH torchvision==$TORCH_VISION torchaudio==$TORCH_AUDIO --index-url https://download.pytorch.org/whl/rocm$ROCM --no-cache-dir + +# Pre-build DeepSpeed, so it's be ready for testing (to avoid timeout) +RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache-dir -v --disable-pip-version-check 2>&1 + +ARG REF=main +WORKDIR / + +# Invalidate docker cache from here if new commit is available. +ADD https://api.github.com/repos/huggingface/transformers/git/refs/heads/main version.json +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN python3 -m pip install --no-cache-dir ./transformers[accelerate,testing,sentencepiece,sklearn] + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +RUN python3 -c "from deepspeed.launcher.runner import main" + +# Remove nvml as it is not compatible with ROCm +RUN python3 -m pip uninstall py3nvml pynvml nvidia-ml-py apex -y diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..36f8506a476b7e7a23790914f8f961a7c76f06e6 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile @@ -0,0 +1,53 @@ +# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-23-11.html#rel-23-11 +FROM nvcr.io/nvidia/pytorch:23.11-py3 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +ARG PYTORCH='2.2.0' +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu121' + +RUN apt -y update +RUN apt install -y libaio-dev +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing] + +# Install latest release PyTorch +# (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.) +# (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops) +RUN python3 -m pip uninstall -y torch torchvision torchaudio && python3 -m pip install --no-cache-dir -U torch==$PYTORCH torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + +# Uninstall `transformer-engine` shipped with the base image +RUN python3 -m pip uninstall -y transformer-engine + +# Uninstall `torch-tensorrt` shipped with the base image +RUN python3 -m pip uninstall -y torch-tensorrt + +# recompile apex +RUN python3 -m pip uninstall -y apex +# RUN git clone https://github.com/NVIDIA/apex +# `MAX_JOBS=1` disables parallel building to avoid cpu memory OOM when building image on GitHub Action (standard) runners +# TODO: check if there is alternative way to install latest apex +# RUN cd apex && MAX_JOBS=1 python3 -m pip install --global-option="--cpp_ext" --global-option="--cuda_ext" --no-cache -v --disable-pip-version-check . + +# Pre-build **latest** DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout) +RUN python3 -m pip uninstall -y deepspeed +# This has to be run (again) inside the GPU VMs running the tests. +# The installation works here, but some tests fail, if we don't pre-build deepspeed again in the VMs running the tests. +# TODO: Find out why test fail. +RUN DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1 + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +# The base image ships with `pydantic==1.8.2` which is not working - i.e. the next command fails +RUN python3 -m pip install -U --no-cache-dir "pydantic>=2.0.0" +RUN python3 -c "from deepspeed.launcher.runner import main" diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..80de73e37c4addaedacea4bf18806725effacd61 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile @@ -0,0 +1,64 @@ +# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-23-11.html#rel-23-11 +FROM nvcr.io/nvidia/pytorch:23.11-py3 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu121' + +RUN apt -y update +RUN apt install -y libaio-dev +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN python3 -m pip uninstall -y torch torchvision torchaudio + +# Install **nightly** release PyTorch (flag `--pre`) +# (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.) +# (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops) +RUN python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA + +RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing] + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + +# Uninstall `transformer-engine` shipped with the base image +RUN python3 -m pip uninstall -y transformer-engine + +# Uninstall `torch-tensorrt` and `apex` shipped with the base image +RUN python3 -m pip uninstall -y torch-tensorrt apex + +# Pre-build **nightly** release of DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout) +RUN python3 -m pip uninstall -y deepspeed +# This has to be run inside the GPU VMs running the tests. (So far, it fails here due to GPU checks during compilation.) +# Issue: https://github.com/deepspeedai/DeepSpeed/issues/2010 +# RUN git clone https://github.com/deepspeedai/DeepSpeed && cd DeepSpeed && rm -rf build && \ +# DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1 + +## For `torchdynamo` tests +## (see https://github.com/huggingface/transformers/pull/17765) +#RUN git clone https://github.com/pytorch/functorch +#RUN python3 -m pip install --no-cache-dir ./functorch[aot] +#RUN cd functorch && python3 setup.py develop +# +#RUN git clone https://github.com/pytorch/torchdynamo +#RUN python3 -m pip install -r ./torchdynamo/requirements.txt +#RUN cd torchdynamo && python3 setup.py develop +# +## install TensorRT +#RUN python3 -m pip install --no-cache-dir -U nvidia-pyindex +#RUN python3 -m pip install --no-cache-dir -U nvidia-tensorrt==8.2.4.2 +# +## install torch_tensorrt (fx path) +#RUN git clone https://github.com/pytorch/TensorRT.git +#RUN cd TensorRT/py && python3 setup.py install --fx-only + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop + +# Disable for now as deepspeed is not installed above. To be enabled once the issue is fixed. +# RUN python3 -c "from deepspeed.launcher.runner import main" diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..7ef08e4d83d8e95625cd7f91583ab79e5e2f12a3 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-gpu/Dockerfile @@ -0,0 +1,33 @@ +FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +# If set to nothing, will install the latest version +ARG PYTORCH='2.6.0' +ARG TORCH_VISION='' +ARG TORCH_AUDIO='' +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu121' + +RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA +RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' || VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA +RUN [ ${#TORCH_AUDIO} -gt 0 ] && VERSION='torchaudio=='TORCH_AUDIO'.*' || VERSION='torchaudio'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA + +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing,video] + +RUN python3 -m pip uninstall -y tensorflow flax + +RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..b61f4add51469b712eebbb0c26d84d6895d6caf2 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/Dockerfile @@ -0,0 +1,65 @@ +FROM google/cloud-sdk:slim + +# Build args. +ARG GITHUB_REF=refs/heads/main + +# TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7 +# wheels available; see below. +ENV PYTHON_VERSION=3.6 + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + cmake \ + git \ + curl \ + ca-certificates + +# Install conda and python. +# NOTE new Conda does not forward the exit status... https://github.com/conda/conda/issues/8385 +RUN curl -o ~/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-4.7.12-Linux-x86_64.sh && \ + chmod +x ~/miniconda.sh && \ + ~/miniconda.sh -b && \ + rm ~/miniconda.sh + +ENV PATH=/root/miniconda3/bin:$PATH + +RUN conda create -y --name container python=$PYTHON_VERSION + +# Run the rest of commands within the new conda env. +# Use absolute path to appease Codefactor. +SHELL ["/root/miniconda3/bin/conda", "run", "-n", "container", "/bin/bash", "-c"] +RUN conda install -y python=$PYTHON_VERSION mkl + +RUN pip uninstall -y torch && \ + # Python 3.7 wheels are available. Replace cp36-cp36m with cp37-cp37m + gsutil cp 'gs://tpu-pytorch/wheels/torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ + gsutil cp 'gs://tpu-pytorch/wheels/torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ + gsutil cp 'gs://tpu-pytorch/wheels/torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' . && \ + pip install 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + pip install 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + pip install 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + rm 'torch-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + rm 'torch_xla-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + rm 'torchvision-nightly-cp${PYTHON_VERSION/./}-cp${PYTHON_VERSION/./}m-linux_x86_64.whl' && \ + apt-get install -y libomp5 + +ENV LD_LIBRARY_PATH=root/miniconda3/envs/container/lib + + +# Install huggingface/transformers at the current PR, plus dependencies. +RUN git clone https://github.com/huggingface/transformers.git && \ + cd transformers && \ + git fetch origin $GITHUB_REF:CI && \ + git checkout CI && \ + cd .. && \ + pip install ./transformers && \ + pip install -r ./transformers/examples/pytorch/_test_requirements.txt && \ + pip install pytest + +RUN python -c "import torch_xla; print(torch_xla.__version__)" +RUN python -c "import transformers as trf; print(trf.__version__)" +RUN conda init bash +COPY docker-entrypoint.sh /usr/local/bin/ +RUN chmod +x /usr/local/bin/docker-entrypoint.sh +ENTRYPOINT ["/usr/local/bin/docker-entrypoint.sh"] +CMD ["bash"] diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet new file mode 100644 index 0000000000000000000000000000000000000000..84608b5d824994646928de1b6d692b03e219c81f --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/bert-base-cased.jsonnet @@ -0,0 +1,38 @@ +local base = import 'templates/base.libsonnet'; +local tpus = import 'templates/tpus.libsonnet'; +local utils = import "templates/utils.libsonnet"; +local volumes = import "templates/volumes.libsonnet"; + +local bertBaseCased = base.BaseTest { + frameworkPrefix: "hf", + modelName: "bert-base-cased", + mode: "example", + configMaps: [], + + timeout: 3600, # 1 hour, in seconds + + image: std.extVar('image'), + imageTag: std.extVar('image-tag'), + + tpuSettings+: { + softwareVersion: "pytorch-nightly", + }, + accelerator: tpus.v3_8, + + volumeMap+: { + datasets: volumes.PersistentVolumeSpec { + name: "huggingface-cluster-disk", + mountPath: "/datasets", + }, + }, + command: utils.scriptCommand( + ||| + python -m pytest -s transformers/examples/pytorch/test_xla_examples.py -v + test_exit_code=$? + echo "\nFinished running commands.\n" + test $test_exit_code -eq 0 + ||| + ), +}; + +bertBaseCased.oneshotJob diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/dataset.yaml b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/dataset.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ce022ea6c18496e209170256b85eae5fa7e7809a --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/dataset.yaml @@ -0,0 +1,32 @@ +apiVersion: v1 +kind: PersistentVolume +metadata: + name: huggingface-cluster-disk +spec: + storageClassName: "" + capacity: + storage: 500Gi + accessModes: + - ReadOnlyMany + claimRef: + namespace: default + name: huggingface-cluster-disk-claim + gcePersistentDisk: + pdName: huggingface-cluster-disk + fsType: ext4 + readOnly: true +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: huggingface-cluster-disk-claim +spec: + # Specify "" as the storageClassName so it matches the PersistentVolume's StorageClass. + # A nil storageClassName value uses the default StorageClass. For details, see + # https://kubernetes.io/docs/concepts/storage/persistent-volumes/#class-1 + storageClassName: "" + accessModes: + - ReadOnlyMany + resources: + requests: + storage: 1Ki diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/docker-entrypoint.sh b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/docker-entrypoint.sh new file mode 100644 index 0000000000000000000000000000000000000000..fbe59566fdcdfd2e61d23288d8da6273003ff9ab --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-pytorch-tpu/docker-entrypoint.sh @@ -0,0 +1,8 @@ +#!/bin/bash +source ~/.bashrc +echo "running docker-entrypoint.sh" +conda activate container +echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS +echo "printed TPU info" +export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}" +exec "$@"#!/bin/bash diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-quantization-latest-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-quantization-latest-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..3887f37b34b3814a87ef68d60a368e0dd7b655f6 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-quantization-latest-gpu/Dockerfile @@ -0,0 +1,84 @@ +FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +# Use login shell to read variables from `~/.profile` (to pass dynamic created variables between RUN commands) +SHELL ["sh", "-lc"] + +# The following `ARG` are mainly used to specify the versions explicitly & directly in this docker file, and not meant +# to be used as arguments for docker build (so far). + +ARG PYTORCH='2.5.1' +# Example: `cu102`, `cu113`, etc. +ARG CUDA='cu118' + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF + +RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' || VERSION='torch'; echo "export VERSION='$VERSION'" >> ~/.profile +RUN echo torch=$VERSION +# `torchvision` and `torchaudio` should be installed along with `torch`, especially for nightly build. +# Currently, let's just use their latest releases (when `torch` is installed with a release version) +RUN python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA + +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch] + +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate + +# needed in bnb and awq +RUN python3 -m pip install --no-cache-dir einops + +# Add bitsandbytes for mixed int8 testing +RUN python3 -m pip install --no-cache-dir bitsandbytes + +# Add auto-gptq for gtpq quantization testing, installed from source for pytorch==2.5.1 compatibility +# TORCH_CUDA_ARCH_LIST="7.5+PTX" is added to make the package compile for Tesla T4 gpus available for the CI. +RUN pip install gekko +RUN git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ && TORCH_CUDA_ARCH_LIST="7.5+PTX" python3 setup.py install + +# Add optimum for gptq quantization testing +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/optimum@main#egg=optimum + +# Add PEFT +RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft@main#egg=peft + +# Add aqlm for quantization testing +RUN python3 -m pip install --no-cache-dir aqlm[gpu]==1.0.2 + +# Add vptq for quantization testing +RUN python3 -m pip install --no-cache-dir vptq + +# Add spqr for quantization testing +RUN python3 -m pip install --no-cache-dir spqr_quant[gpu] + +# Add hqq for quantization testing +RUN python3 -m pip install --no-cache-dir hqq + +# For GGUF tests +RUN python3 -m pip install --no-cache-dir gguf + +# Add autoawq for quantization testing +# >=v0.2.7 needed for compatibility with transformers > 4.46 +RUN python3 -m pip install --no-cache-dir https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.7.post2/autoawq-0.2.7.post2-py3-none-any.whl + +# Add quanto for quantization testing +RUN python3 -m pip install --no-cache-dir optimum-quanto + +# Add eetq for quantization testing +RUN python3 -m pip install git+https://github.com/NetEase-FuXi/EETQ.git + +# Add flute-kernel and fast_hadamard_transform for quantization testing +RUN python3 -m pip install --no-cache-dir flute-kernel==0.3.0 -i https://flute-ai.github.io/whl/cu118 +RUN python3 -m pip install --no-cache-dir fast_hadamard_transform==1.0.4.post1 + +# Add compressed-tensors for quantization testing +RUN python3 -m pip install --no-cache-dir compressed-tensors + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-tensorflow-gpu/Dockerfile b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-tensorflow-gpu/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..378491a6c600079b79d5d63b06e2d6f873d4b7f3 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docker/transformers-tensorflow-gpu/Dockerfile @@ -0,0 +1,25 @@ +FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu22.04 +LABEL maintainer="Hugging Face" + +ARG DEBIAN_FRONTEND=noninteractive + +RUN apt update +RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg +RUN python3 -m pip install --no-cache-dir --upgrade pip + +ARG REF=main +RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF +RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing] + +# If set to nothing, will install the latest version +ARG TENSORFLOW='2.13' + +RUN [ ${#TENSORFLOW} -gt 0 ] && VERSION='tensorflow=='$TENSORFLOW'.*' || VERSION='tensorflow'; python3 -m pip install --no-cache-dir -U $VERSION +RUN python3 -m pip uninstall -y torch flax +RUN python3 -m pip install -U "itsdangerous<2.1.0" + +RUN python3 -m pip install --no-cache-dir -U "tensorflow_probability<0.22" + +# When installing in editable mode, `transformers` is not recognized as a package. +# this line must be added in order for python to be aware of transformers. +RUN cd transformers && python3 setup.py develop diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docs/README.md b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bb54d7004130f206ab90d96dc3917259af257889 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/README.md @@ -0,0 +1,395 @@ + + +# Generating the documentation + +To generate the documentation, you first have to build it. Several packages are necessary to build the doc, +you can install them with the following command, at the root of the code repository: + +```bash +pip install -e ".[docs]" +``` + +Then you need to install our special tool that builds the documentation: + +```bash +pip install git+https://github.com/huggingface/doc-builder +``` + +--- +**NOTE** + +You only need to generate the documentation to inspect it locally (if you're planning changes and want to +check how they look before committing for instance). You don't have to commit the built documentation. + +--- + +## Building the documentation + +Once you have setup the `doc-builder` and additional packages, you can generate the documentation by +typing the following command: + +```bash +doc-builder build transformers docs/source/en/ --build_dir ~/tmp/test-build +``` + +You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate +the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite +Markdown editor. + +## Previewing the documentation + +To preview the docs, first install the `watchdog` module with: + +```bash +pip install watchdog +``` + +Then run the following command: + +```bash +doc-builder preview {package_name} {path_to_docs} +``` + +For example: + +```bash +doc-builder preview transformers docs/source/en/ +``` + +The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives. + +--- +**NOTE** + +The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again). + +--- + +## Adding a new element to the navigation bar + +Accepted files are Markdown (.md). + +Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting +the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml) file. + +## Renaming section headers and moving sections + +It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information. + +Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor. + +So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file: + +``` +Sections that were moved: + +[ Section A ] +``` +and of course, if you moved it to another file, then: + +``` +Sections that were moved: + +[ Section A ] +``` + +Use the relative style to link to the new file so that the versioned docs continue to work. + +For an example of a rich moved section set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md). + + +## Writing Documentation - Specification + +The `huggingface/transformers` documentation follows the +[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings, +although we can write them directly in Markdown. + +### Adding a new tutorial + +Adding a new tutorial or section is done in two steps: + +- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md). +- Link that file in `./source/_toctree.yml` on the correct toc-tree. + +Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so +depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or +four. + +### Translating + +When translating, refer to the guide at [./TRANSLATING.md](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). + + +### Adding a new model + +When adding a new model: + +- Create a file `xxx.md` or under `./source/model_doc` (don't hesitate to copy an existing file as template). +- Link that file in `./source/_toctree.yml`. +- Write a short overview of the model: + - Overview with paper & authors + - Paper abstract + - Tips and tricks and how to use it best +- Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and + every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow. + The order is generally: + - Configuration + - Tokenizer + - PyTorch base model + - PyTorch head models + - TensorFlow base model + - TensorFlow head models + - Flax base model + - Flax head models + +These classes should be added using our Markdown syntax. Usually as follows: + +``` +## XXXConfig + +[[autodoc]] XXXConfig +``` + +This will include every public method of the configuration that is documented. If for some reason you wish for a method +not to be displayed in the documentation, you can do so by specifying which methods should be in the docs: + +``` +## XXXTokenizer + +[[autodoc]] XXXTokenizer + - build_inputs_with_special_tokens + - get_special_tokens_mask + - create_token_type_ids_from_sequences + - save_vocabulary +``` + +If you just want to add a method that is not documented (for instance magic methods like `__call__` are not documented +by default) you can put the list of methods to add in a list that contains `all`: + +``` +## XXXTokenizer + +[[autodoc]] XXXTokenizer + - all + - __call__ +``` + +### Writing source documentation + +Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names +and objects like True, None, or any strings should usually be put in `code`. + +When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool +adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or +function to be in the main package. + +If you want to create a link to some internal class or function, you need to +provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with +`utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are +linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description. + +The same works for methods so you can either use \[\`XXXClass.method\`\] or \[\`~XXXClass.method\`\]. + +#### Defining arguments in a method + +Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and +an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its +description: + +``` + Args: + n_layers (`int`): The number of layers of the model. +``` + +If the description is too long to fit in one line, another indentation is necessary before writing the description +after the argument. + +Here's an example showcasing everything so far: + +``` + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and + [`~PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) +``` + +For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the +following signature: + +``` +def my_function(x: str = None, a: float = 1): +``` + +then its documentation should look like this: + +``` + Args: + x (`str`, *optional*): + This argument controls ... + a (`float`, *optional*, defaults to 1): + This argument is used to ... +``` + +Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even +if the first line describing your argument type and its default gets long, you can't break it on several lines. You can +however, write as many lines as you want in the indented description (see the example above with `input_ids`). + +#### Writing a multi-line code block + +Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown: + + +```` +``` +# first line of code +# second line +# etc +``` +```` + +We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test +the results to stay consistent with the library. + +#### Writing a return block + +The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation. +The first line should be the type of the return, followed by a line return. No need to indent further for the elements +building the return. + +Here's an example of a single value return: + +```python + Returns: + `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token. +``` + +Here's an example of a tuple return, comprising several objects: + +```python + Returns: + `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs: + - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` -- + Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss. + - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). +``` + +#### Adding an image + +Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like +the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference +them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). +If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images +to this dataset. + +## Styling the docstring + +We have an automatic script running with the `make style` comment that will make sure that: +- the docstrings fully take advantage of the line width +- all code examples are formatted using black, like the code of the Transformers library + +This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's +recommended to commit your changes before running `make style`, so you can revert the changes done by that script +easily. + +# Testing documentation examples + +Good documentation often comes with an example of how a specific function or class should be used. +Each model class should contain at least one example showcasing +how to use this model class in inference. *E.g.* the class [Wav2Vec2ForCTC](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC) +includes an example of how to transcribe speech to text in the +[docstring of its forward function](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.forward). + +## Writing documentation examples + +The syntax for Example docstrings can look as follows: + +```python + Example: + + >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC + >>> from datasets import load_dataset + >>> import torch + + >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") + >>> dataset = dataset.sort("id") + >>> sampling_rate = dataset.features["audio"].sampling_rate + + >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") + >>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") + + >>> # audio file is decoded on the fly + >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") + >>> with torch.no_grad(): + ... logits = model(**inputs).logits + >>> predicted_ids = torch.argmax(logits, dim=-1) + + >>> # transcribe speech + >>> transcription = processor.batch_decode(predicted_ids) + >>> transcription[0] + 'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL' +``` + +The docstring should give a minimal, clear example of how the respective model +is to be used in inference and also include the expected (ideally sensible) +output. +Often, readers will try out the example before even going through the function +or class definitions. Therefore, it is of utmost importance that the example +works as expected. + +## Docstring testing + +To do so each example should be included in the doctests. +We use pytests' [doctest integration](https://docs.pytest.org/doctest.html) to verify that all of our examples run correctly. +For Transformers, the doctests are run on a daily basis via GitHub Actions as can be +seen [here](https://github.com/huggingface/transformers/actions/workflows/doctests.yml). + +### For Python files + +Run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance: + +```bash +pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py -sv --doctest-continue-on-failure +``` + +If you want to isolate a specific docstring, just add `::` after the file name then type the whole path of the function/class/method whose docstring you want to test. For instance, here is how to just test the forward method of `Wav2Vec2ForCTC`: + +```bash +pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py::transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward -sv --doctest-continue-on-failure +``` + +### For Markdown files + +You can test locally a given file with this command (here testing the quicktour): + +```bash +pytest --doctest-modules docs/source/quicktour.md -sv --doctest-continue-on-failure --doctest-glob="*.md" +``` + +### Writing doctests + +Here are a few tips to help you debug the doctests and make them pass: + +- The outputs of the code need to match the expected output **exactly**, so make sure you have the same outputs. In particular doctest will see a difference between single quotes and double quotes, or a missing parenthesis. The only exceptions to that rule are: + * whitespace: one give whitespace (space, tabulation, new line) is equivalent to any number of whitespace, so you can add new lines where there are spaces to make your output more readable. + * numerical values: you should never put more than 4 or 5 digits to expected results as different setups or library versions might get you slightly different results. `doctest` is configured to ignore any difference lower than the precision to which you wrote (so 1e-4 if you write 4 digits). +- Don't leave a block of code that is very long to execute. If you can't make it fast, you can either not use the doctest syntax on it (so that it's ignored), or if you want to use the doctest syntax to show the results, you can add a comment `# doctest: +SKIP` at the end of the lines of code too long to execute +- Each line of code that produces a result needs to have that result written below. You can ignore an output if you don't want to show it in your code example by adding a comment ` # doctest: +IGNORE_RESULT` at the end of the line of code producing it. diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docs/TRANSLATING.md b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/TRANSLATING.md new file mode 100644 index 0000000000000000000000000000000000000000..64dced450987dc42c4f9f63ac3d2f41f04e570c9 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/TRANSLATING.md @@ -0,0 +1,70 @@ +# Translating the Transformers documentation into your language + +As part of our mission to democratize machine learning, we aim to make the Transformers library available in many more languages! Follow the steps below to help translate the documentation into your language. + +## Open an Issue + +1. Navigate to the Issues page of this repository. +2. Check if anyone has already opened an issue for your language. +3. If not, create a new issue by selecting the "Translation template" from the "New issue" button. +4. Post a comment indicating which chapters you’d like to work on, and we’ll add your name to the list. + +## Fork the Repository + +1. First, fork the Transformers repo by clicking the Fork button in the top-right corner. +2. Clone your fork to your local machine for editing with the following command: + + ```bash + git clone https://github.com/YOUR-USERNAME/transformers.git + ``` + + Replace `YOUR-USERNAME` with your GitHub username. + +## Copy-paste the English version with a new language code + +The documentation files are organized in the following directory: + +- **docs/source**: This contains all documentation materials organized by language. + +To copy the English version to your new language directory: + +1. Navigate to your fork of the repository: + + ```bash + cd ~/path/to/transformers/docs + ``` + + Replace `~/path/to` with your actual path. + +2. Run the following command: + + ```bash + cp -r source/en source/LANG-ID + ``` + + Replace `LANG-ID` with the appropriate ISO 639-1 or ISO 639-2 language code (see [this table](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) for reference). + +## Start translating + +Begin translating the text! + +1. Start with the `_toctree.yml` file that corresponds to your documentation chapter. This file is essential for rendering the table of contents on the website. + + - If the `_toctree.yml` file doesn’t exist for your language, create one by copying the English version and removing unrelated sections. + - Ensure it is placed in the `docs/source/LANG-ID/` directory. + + Here’s an example structure for the `_toctree.yml` file: + + ```yaml + - sections: + - local: pipeline_tutorial # Keep this name for your .md file + title: Pipelines for Inference # Translate this + ... + title: Tutorials # Translate this + ``` + +2. Once you’ve translated the `_toctree.yml`, move on to translating the associated MDX files. + +## Collaborate and share + +If you'd like assistance with your translation, open an issue and tag `@stevhliu`. Feel free to share resources or glossaries to ensure consistent terminology. diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/_config.py b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/_config.py new file mode 100644 index 0000000000000000000000000000000000000000..f49e4e4731965a504b8da443c2cd979638cd22bb --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/_config.py @@ -0,0 +1,14 @@ +# docstyle-ignore +INSTALL_CONTENT = """ +# Transformers installation +! pip install transformers datasets evaluate accelerate +# To install from source instead of the last release, comment the command above and uncomment the following one. +# ! pip install git+https://github.com/huggingface/transformers.git +""" + +notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] +black_avoid_patterns = { + "{processor_class}": "FakeProcessorClass", + "{model_class}": "FakeModelClass", + "{object_class}": "FakeObjectClass", +} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/_config.py b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/_config.py new file mode 100644 index 0000000000000000000000000000000000000000..f49e4e4731965a504b8da443c2cd979638cd22bb --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/_config.py @@ -0,0 +1,14 @@ +# docstyle-ignore +INSTALL_CONTENT = """ +# Transformers installation +! pip install transformers datasets evaluate accelerate +# To install from source instead of the last release, comment the command above and uncomment the following one. +# ! pip install git+https://github.com/huggingface/transformers.git +""" + +notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] +black_avoid_patterns = { + "{processor_class}": "FakeProcessorClass", + "{model_class}": "FakeModelClass", + "{object_class}": "FakeObjectClass", +} diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/_toctree.yml b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/_toctree.yml new file mode 100644 index 0000000000000000000000000000000000000000..30e247eb54e19747c815019934b5744154780614 --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/_toctree.yml @@ -0,0 +1,898 @@ +- sections: + - local: index + title: 🤗 المحولات + - local: quicktour + title: جولة سريعة + - local: installation + title: التثبيت + title: البدء +- sections: + - local: pipeline_tutorial + title: تشغيل الاستنتاج باستخدام خطوط الأنابيب + - local: autoclass_tutorial + title: كتابة تعليمات برمجية متكيفه باستخدام AutoClass + - local: preprocessing + title: معالجة البيانات مسبقًا + - local: training + title: ضبط نموذج مسبق التدريب + - local: run_scripts + title: التدريب باستخدام نص برمجي + - local: accelerate + title: إعداد تدريب موزع باستخدام 🤗 Accelerate + - local: peft + title: تحميل النماذج المخصصة وتدريبها باستخدام 🤗 PEFT + - local: model_sharing + title: مشاركة نموذجك + - local: agents + title: الوكلاء + - local: llm_tutorial + title: التوليد باستخدام LLMs + - local: conversations + title: الدردشة مع المحولات + title: البرامج التعليمية +- sections: + - isExpanded: false + sections: + - local: tasks/sequence_classification + title: تصنيف النصوص + - local: tasks/token_classification + title: تصنيف الرموز + - local: tasks/question_answering + title: الإجابة على الأسئلة + - local: tasks/language_modeling + title: نمذجة اللغة السببية + - local: tasks/masked_language_modeling + title: نمذجة اللغة المقنعة + - local: tasks/translation + title: الترجمة + - local: tasks/summarization + title: التلخيص + - local: tasks/multiple_choice + title: الاختيار المتعدد + title: معالجة اللغات الطبيعية +# - isExpanded: false +# sections: +# - local: tasks/audio_classification +# title: تصنيف الصوت +# - local: tasks/asr +# title: التعرف التلقائي على الكلام +# title: الصوت +# - isExpanded: false +# sections: +# - local: tasks/image_classification +# title: تصنيف الصور +# - local: tasks/semantic_segmentation +# title: تجزئة الصور +# - local: tasks/video_classification +# title: تصنيف الفيديو +# - local: tasks/object_detection +# title: اكتشاف الأشياء +# - local: tasks/zero_shot_object_detection +# title: اكتشاف الأشياء بدون تدريب +# - local: tasks/zero_shot_image_classification +# title: تصنيف الصور بدون تدريب +# - local: tasks/monocular_depth_estimation +# title: تقدير العمق +# - local: tasks/image_to_image +# title: صورة إلى صورة +# - local: tasks/image_feature_extraction +# title: استخراج ميزات الصورة +# - local: tasks/mask_generation +# title: توليد القناع +# - local: tasks/knowledge_distillation_for_image_classification +# title: التقليل المعرفي للرؤية الحاسوبية +# title: الرؤية الحاسوبية +# - isExpanded: false +# sections: +# - local: tasks/image_captioning +# title: وصف الصور Image captioning +# - local: tasks/document_question_answering +# title: الإجابة على أسئلة المستندات +# - local: tasks/visual_question_answering +# title: الإجابة على الأسئلة المرئية +# - local: tasks/text-to-speech +# title: تحويل النص إلى كلام +# title: المتعددة الوسائط +# - isExpanded: false +# sections: +# - local: generation_strategies +# title: تخصيص استراتيجية التوليد +# - local: kv_cache +# title: أفضل الممارسات للتوليد باستخدام ذاكرة التخزين المؤقت +# title: التوليد +# - isExpanded: false +# sections: +# - local: tasks/idefics +# title: مهام الصور مع IDEFICS +# - local: tasks/prompting +# title: دليل إرشادي لمحفزات النماذج اللغوية الكبيرة +# title: الإرشاد + title: أدلة المهام +- sections: + - local: fast_tokenizers + title: استخدم مجزئيات النصوص السريعة من 🤗 Tokenizers + - local: multilingual + title: الاستدلال باستخدام نماذج متعددة اللغات + - local: create_a_model + title: استخدام واجهات برمجة التطبيقات الخاصة بالنموذج + - local: custom_models + title: مشاركة نموذج مخصص + - local: chat_templating + title: قوالب لنماذج الدردشة + - local: trainer + title: المدرب + - local: sagemaker + title: تشغيل التدريب على Amazon SageMaker + - local: serialization + title: التصدير إلى ONNX + - local: tflite + title: التصدير إلى TFLite + - local: torchscript + title: التصدير إلى TorchScript + - local: notebooks + title: دفاتر الملاحظات مع الأمثلة + - local: community + title: موارد المجتمع + - local: troubleshooting + title: استكشاف الأخطاء وإصلاحها + - local: gguf + title: التوافق مع ملفات GGUF + - local: tiktoken + title: التوافق مع ملفات TikToken + - local: modular_transformers + title: الوحدات النمطية في `transformers` + - local: how_to_hack_models + title: اختراق النموذج (الكتابة فوق فئة لاستخدامك) + title: أدلة المطورين +# - sections: +# - local: quantization/overview +# title: نظرة عامة +# - local: quantization/bitsandbytes +# title: bitsandbytes +# - local: quantization/gptq +# title: GPTQ +# - local: quantization/awq +# title: AWQ +# - local: quantization/aqlm +# title: AQLM +# - local: quantization/vptq +# title: VPTQ +# - local: quantization/quanto +# title: Quanto +# - local: quantization/eetq +# title: EETQ +# - local: quantization/hqq +# title: HQQ +# - local: quantization/optimum +# title: Optimum +# - local: quantization/contribute +# title: المساهمة بطريقة جديدة للتكميم +# title: أساليب التكميم +# - sections: +# - local: performance +# title: الأداء-نظرة عامة +# - local: llm_optims +# title: تحسين الاستدلال LLM +# - sections: +# - local: perf_train_gpu_one +# title: استخدام عدة وحدات معالجة رسوميات (GPUs) بشكل متوازٍ +# - local: perf_train_gpu_many +# title: وحدات معالجة الرسومات (GPU) متعددة والتوازي +# - local: fsdp +# title: Fully Sharded Data Parallel +# - local: deepspeed +# title: DeepSpeed +# - local: perf_train_cpu +# title: التدريب الفعال على وحدة المعالجة المركزية (CPU) +# - local: perf_train_cpu_many +# title: التدريب الموزع لوحدة المعالجة المركزية (CPU) +# - local: perf_train_tpu_tf +# title: التدريب على (TPU) باستخدام TensorFlow +# - local: perf_train_special +# title: تدريب PyTorch على Apple silicon +# - local: perf_hardware +# title: الأجهزة المخصصة للتدريب +# - local: hpo_train +# title: البحث عن المعاملات المثلى باستخدام واجهة برمجة تطبيقات المدرب +# title: تقنيات التدريب الفعال +# - sections: +# - local: perf_infer_cpu +# title: الإستدلال على وحدة المعالجة المركزية (CPU) +# - local: perf_infer_gpu_one +# title: الإستدلال على وحدة معالجة الرسومات (GPU) +# title: تحسين الاستدلال +# - local: big_models +# title: إنشاء نموذج كبير +# - local: debugging +# title: تصحيح الأخطاء البرمجية +# - local: tf_xla +# title: تكامل XLA لنماذج TensorFlow +# - local: perf_torch_compile +# title: تحسين الاستدلال باستخدام `torch.compile()` +# title: الأداء وقابلية التوسع +# - sections: +# - local: contributing +# title: كيفية المساهمة في 🤗 المحولات؟ +# - local: add_new_model +# title: كيفية إضافة نموذج إلى 🤗 المحولات؟ +# - local: add_new_pipeline +# title: كيفية إضافة خط أنابيب إلى 🤗 المحولات؟ +# - local: testing +# title: الاختبار +# - local: pr_checks +# title: التحقق من طلب السحب +# title: المساهمة +- sections: + - local: philosophy + title: الفلسفة + - local: glossary + title: (قاموس المصطلحات (قائمة الكلمات + - local: task_summary + title: ما الذي يمكن أن تفعله 🤗 المحولات + - local: tasks_explained + title: كيف تحل المحولات المهام + - local: model_summary + title: عائلة نماذج المحول + - local: tokenizer_summary + title: ملخص برنامج مقسم النصوص (tokenizers) + - local: attention + title: الانتباه Attention + - local: pad_truncation + title: الحشو والتقليم + - local: bertology + title: BERTology + - local: perplexity + title: حيرة النماذج ذات الطول الثابت + - local: pipeline_webserver + title: خطوط الأنابيب للاستدلال على خادم الويب + - local: model_memory_anatomy + title: تشريح تدريب النموذج + - local: llm_tutorial_optimization + title: الاستفادة القصوى من LLMs + title: أطر مفاهيمية +# - sections: +# - sections: +# - local: main_classes/agent +# title: الوكلاء والأدوات +# - local: model_doc/auto +# title: فئات يتم إنشاؤها ديناميكيًا +# - local: main_classes/backbones +# title: العمود الفقري +# - local: main_classes/callback +# title: عمليات الاسترجاع +# - local: main_classes/configuration +# title: التكوين +# - local: main_classes/data_collator +# title: مجمع البيانات +# - local: main_classes/keras_callbacks +# title: استدعاءات Keras +# - local: main_classes/logging +# title: التسجيل +# - local: main_classes/model +# title: النماذج +# - local: main_classes/text_generation +# title: توليد النصوص +# - local: main_classes/onnx +# title: ONNX +# - local: main_classes/optimizer_schedules +# title: التحسين +# - local: main_classes/output +# title: مخرجات النموذج +# - local: main_classes/pipelines +# title: خطوط الأنابيب +# - local: main_classes/processors +# title: المعالجات +# - local: main_classes/quantization +# title: التكميم +# - local: main_classes/tokenizer +# title: برنامج مقسم النصوص +# - local: main_classes/trainer +# title: المدرب +# - local: main_classes/deepspeed +# title: DeepSpeed +# - local: main_classes/feature_extractor +# title: مستخرج الميزات +# - local: main_classes/image_processor +# title: معالج الصور +# title: الفئات الرئيسية +# - sections: +# - isExpanded: false +# sections: +# - local: model_doc/albert +# title: ALBERT +# - local: model_doc/bart +# title: BART +# - local: model_doc/barthez +# title: BARThez +# - local: model_doc/bartpho +# title: BARTpho +# - local: model_doc/bert +# title: BERT +# - local: model_doc/bert-generation +# title: BertGeneration +# - local: model_doc/bert-japanese +# title: BertJapanese +# - local: model_doc/bertweet +# title: Bertweet +# - local: model_doc/big_bird +# title: BigBird +# - local: model_doc/bigbird_pegasus +# title: BigBirdPegasus +# - local: model_doc/biogpt +# title: BioGpt +# - local: model_doc/blenderbot +# title: Blenderbot +# - local: model_doc/blenderbot-small +# title: Blenderbot Small +# - local: model_doc/bloom +# title: BLOOM +# - local: model_doc/bort +# title: BORT +# - local: model_doc/byt5 +# title: ByT5 +# - local: model_doc/camembert +# title: CamemBERT +# - local: model_doc/canine +# title: CANINE +# - local: model_doc/codegen +# title: CodeGen +# - local: model_doc/code_llama +# title: CodeLlama +# - local: model_doc/cohere +# title: Cohere +# - local: model_doc/convbert +# title: ConvBERT +# - local: model_doc/cpm +# title: CPM +# - local: model_doc/cpmant +# title: CPMANT +# - local: model_doc/ctrl +# title: CTRL +# - local: model_doc/dbrx +# title: DBRX +# - local: model_doc/deberta +# title: DeBERTa +# - local: model_doc/deberta-v2 +# title: DeBERTa-v2 +# - local: model_doc/dialogpt +# title: DialoGPT +# - local: model_doc/distilbert +# title: DistilBERT +# - local: model_doc/dpr +# title: DPR +# - local: model_doc/electra +# title: ELECTRA +# - local: model_doc/encoder-decoder +# title: Encoder Decoder Models +# - local: model_doc/ernie +# title: ERNIE +# - local: model_doc/ernie_m +# title: ErnieM +# - local: model_doc/esm +# title: ESM +# - local: model_doc/falcon +# title: Falcon +# - local: model_doc/fastspeech2_conformer +# title: FastSpeech2Conformer +# - local: model_doc/flan-t5 +# title: FLAN-T5 +# - local: model_doc/flan-ul2 +# title: FLAN-UL2 +# - local: model_doc/flaubert +# title: FlauBERT +# - local: model_doc/fnet +# title: FNet +# - local: model_doc/fsmt +# title: FSMT +# - local: model_doc/funnel +# title: Funnel Transformer +# - local: model_doc/fuyu +# title: Fuyu +# - local: model_doc/gemma +# title: Gemma +# - local: model_doc/openai-gpt +# title: GPT +# - local: model_doc/gpt_neo +# title: GPT Neo +# - local: model_doc/gpt_neox +# title: GPT NeoX +# - local: model_doc/gpt_neox_japanese +# title: GPT NeoX Japanese +# - local: model_doc/gptj +# title: GPT-J +# - local: model_doc/gpt2 +# title: GPT2 +# - local: model_doc/gpt_bigcode +# title: GPTBigCode +# - local: model_doc/gptsan-japanese +# title: GPTSAN Japanese +# - local: model_doc/gpt-sw3 +# title: GPTSw3 +# - local: model_doc/herbert +# title: HerBERT +# - local: model_doc/ibert +# title: I-BERT +# - local: model_doc/jamba +# title: Jamba +# - local: model_doc/jetmoe +# title: JetMoe +# - local: model_doc/jukebox +# title: Jukebox +# - local: model_doc/led +# title: LED +# - local: model_doc/llama +# title: LLaMA +# - local: model_doc/llama2 +# title: Llama2 +# - local: model_doc/llama3 +# title: Llama3 +# - local: model_doc/longformer +# title: Longformer +# - local: model_doc/longt5 +# title: LongT5 +# - local: model_doc/luke +# title: LUKE +# - local: model_doc/m2m_100 +# title: M2M100 +# - local: model_doc/madlad-400 +# title: MADLAD-400 +# - local: model_doc/mamba +# title: Mamba +# - local: model_doc/marian +# title: MarianMT +# - local: model_doc/markuplm +# title: MarkupLM +# - local: model_doc/mbart +# title: MBart and MBart-50 +# - local: model_doc/mega +# title: MEGA +# - local: model_doc/megatron-bert +# title: MegatronBERT +# - local: model_doc/megatron_gpt2 +# title: MegatronGPT2 +# - local: model_doc/mistral +# title: Mistral +# - local: model_doc/mixtral +# title: Mixtral +# - local: model_doc/mluke +# title: mLUKE +# - local: model_doc/mobilebert +# title: MobileBERT +# - local: model_doc/mpnet +# title: MPNet +# - local: model_doc/mpt +# title: MPT +# - local: model_doc/mra +# title: MRA +# - local: model_doc/mt5 +# title: MT5 +# - local: model_doc/mvp +# title: MVP +# - local: model_doc/nezha +# title: NEZHA +# - local: model_doc/nllb +# title: NLLB +# - local: model_doc/nllb-moe +# title: NLLB-MoE +# - local: model_doc/nystromformer +# title: Nyströmformer +# - local: model_doc/olmo +# title: OLMo +# - local: model_doc/open-llama +# title: Open-Llama +# - local: model_doc/opt +# title: OPT +# - local: model_doc/pegasus +# title: Pegasus +# - local: model_doc/pegasus_x +# title: PEGASUS-X +# - local: model_doc/persimmon +# title: Persimmon +# - local: model_doc/phi +# title: Phi +# - local: model_doc/phi3 +# title: Phi-3 +# - local: model_doc/phobert +# title: PhoBERT +# - local: model_doc/plbart +# title: PLBart +# - local: model_doc/prophetnet +# title: ProphetNet +# - local: model_doc/qdqbert +# title: QDQBert +# - local: model_doc/qwen2 +# title: Qwen2 +# - local: model_doc/qwen2_moe +# title: Qwen2MoE +# - local: model_doc/rag +# title: RAG +# - local: model_doc/realm +# title: REALM +# - local: model_doc/recurrent_gemma +# title: RecurrentGemma +# - local: model_doc/reformer +# title: Reformer +# - local: model_doc/rembert +# title: RemBERT +# - local: model_doc/retribert +# title: RetriBERT +# - local: model_doc/roberta +# title: RoBERTa +# - local: model_doc/roberta-prelayernorm +# title: RoBERTa-PreLayerNorm +# - local: model_doc/roc_bert +# title: RoCBert +# - local: model_doc/roformer +# title: RoFormer +# - local: model_doc/rwkv +# title: RWKV +# - local: model_doc/splinter +# title: Splinter +# - local: model_doc/squeezebert +# title: SqueezeBERT +# - local: model_doc/stablelm +# title: StableLm +# - local: model_doc/starcoder2 +# title: Starcoder2 +# - local: model_doc/switch_transformers +# title: SwitchTransformers +# - local: model_doc/t5 +# title: T5 +# - local: model_doc/t5v1.1 +# title: T5v1.1 +# - local: model_doc/tapex +# title: TAPEX +# - local: model_doc/transfo-xl +# title: Transformer XL +# - local: model_doc/ul2 +# title: UL2 +# - local: model_doc/umt5 +# title: UMT5 +# - local: model_doc/xmod +# title: X-MOD +# - local: model_doc/xglm +# title: XGLM +# - local: model_doc/xlm +# title: XLM +# - local: model_doc/xlm-prophetnet +# title: XLM-ProphetNet +# - local: model_doc/xlm-roberta +# title: XLM-RoBERTa +# - local: model_doc/xlm-roberta-xl +# title: XLM-RoBERTa-XL +# - local: model_doc/xlm-v +# title: XLM-V +# - local: model_doc/xlnet +# title: XLNet +# - local: model_doc/yoso +# title: YOSO +# title: Text models +# - isExpanded: false +# sections: +# - local: model_doc/beit +# title: BEiT +# - local: model_doc/bit +# title: BiT +# - local: model_doc/conditional_detr +# title: Conditional DETR +# - local: model_doc/convnext +# title: ConvNeXT +# - local: model_doc/convnextv2 +# title: ConvNeXTV2 +# - local: model_doc/cvt +# title: CVT +# - local: model_doc/deformable_detr +# title: Deformable DETR +# - local: model_doc/deit +# title: DeiT +# - local: model_doc/depth_anything +# title: Depth Anything +# - local: model_doc/deta +# title: DETA +# - local: model_doc/detr +# title: DETR +# - local: model_doc/dinat +# title: DiNAT +# - local: model_doc/dinov2 +# title: DINOV2 +# - local: model_doc/dit +# title: DiT +# - local: model_doc/dpt +# title: DPT +# - local: model_doc/efficientformer +# title: EfficientFormer +# - local: model_doc/efficientnet +# title: EfficientNet +# - local: model_doc/focalnet +# title: FocalNet +# - local: model_doc/glpn +# title: GLPN +# - local: model_doc/imagegpt +# title: ImageGPT +# - local: model_doc/levit +# title: LeViT +# - local: model_doc/mask2former +# title: Mask2Former +# - local: model_doc/maskformer +# title: MaskFormer +# - local: model_doc/mobilenet_v1 +# title: MobileNetV1 +# - local: model_doc/mobilenet_v2 +# title: MobileNetV2 +# - local: model_doc/mobilevit +# title: MobileViT +# - local: model_doc/mobilevitv2 +# title: MobileViTV2 +# - local: model_doc/nat +# title: NAT +# - local: model_doc/poolformer +# title: PoolFormer +# - local: model_doc/pvt +# title: Pyramid Vision Transformer (PVT) +# - local: model_doc/pvt_v2 +# title: Pyramid Vision Transformer v2 (PVTv2) +# - local: model_doc/regnet +# title: RegNet +# - local: model_doc/resnet +# title: ResNet +# - local: model_doc/segformer +# title: SegFormer +# - local: model_doc/seggpt +# title: SegGpt +# - local: model_doc/superpoint +# title: SuperPoint +# - local: model_doc/swiftformer +# title: SwiftFormer +# - local: model_doc/swin +# title: Swin Transformer +# - local: model_doc/swinv2 +# title: Swin Transformer V2 +# - local: model_doc/swin2sr +# title: Swin2SR +# - local: model_doc/table-transformer +# title: Table Transformer +# - local: model_doc/upernet +# title: UperNet +# - local: model_doc/van +# title: VAN +# - local: model_doc/vit +# title: Vision Transformer (ViT) +# - local: model_doc/vit_hybrid +# title: ViT Hybrid +# - local: model_doc/vitdet +# title: ViTDet +# - local: model_doc/vit_mae +# title: ViTMAE +# - local: model_doc/vitmatte +# title: ViTMatte +# - local: model_doc/vit_msn +# title: ViTMSN +# - local: model_doc/yolos +# title: YOLOS +# title: Vision models +# - isExpanded: false +# sections: +# - local: model_doc/audio-spectrogram-transformer +# title: Audio Spectrogram Transformer +# - local: model_doc/bark +# title: Bark +# - local: model_doc/clap +# title: CLAP +# - local: model_doc/encodec +# title: EnCodec +# - local: model_doc/hubert +# title: Hubert +# - local: model_doc/mctct +# title: MCTCT +# - local: model_doc/mms +# title: MMS +# - local: model_doc/musicgen +# title: MusicGen +# - local: model_doc/musicgen_melody +# title: MusicGen Melody +# - local: model_doc/pop2piano +# title: Pop2Piano +# - local: model_doc/seamless_m4t +# title: Seamless-M4T +# - local: model_doc/seamless_m4t_v2 +# title: SeamlessM4T-v2 +# - local: model_doc/sew +# title: SEW +# - local: model_doc/sew-d +# title: SEW-D +# - local: model_doc/speech_to_text +# title: Speech2Text +# - local: model_doc/speech_to_text_2 +# title: Speech2Text2 +# - local: model_doc/speecht5 +# title: SpeechT5 +# - local: model_doc/unispeech +# title: UniSpeech +# - local: model_doc/unispeech-sat +# title: UniSpeech-SAT +# - local: model_doc/univnet +# title: UnivNet +# - local: model_doc/vits +# title: VITS +# - local: model_doc/wav2vec2 +# title: Wav2Vec2 +# - local: model_doc/wav2vec2-bert +# title: Wav2Vec2-BERT +# - local: model_doc/wav2vec2-conformer +# title: Wav2Vec2-Conformer +# - local: model_doc/wav2vec2_phoneme +# title: Wav2Vec2Phoneme +# - local: model_doc/wavlm +# title: WavLM +# - local: model_doc/whisper +# title: Whisper +# - local: model_doc/xls_r +# title: XLS-R +# - local: model_doc/xlsr_wav2vec2 +# title: XLSR-Wav2Vec2 +# title: Audio models +# - isExpanded: false +# sections: +# - local: model_doc/timesformer +# title: TimeSformer +# - local: model_doc/videomae +# title: VideoMAE +# - local: model_doc/vivit +# title: ViViT +# title: Video models +# - isExpanded: false +# sections: +# - local: model_doc/align +# title: ALIGN +# - local: model_doc/altclip +# title: AltCLIP +# - local: model_doc/blip +# title: BLIP +# - local: model_doc/blip-2 +# title: BLIP-2 +# - local: model_doc/bridgetower +# title: BridgeTower +# - local: model_doc/bros +# title: BROS +# - local: model_doc/chinese_clip +# title: Chinese-CLIP +# - local: model_doc/clip +# title: CLIP +# - local: model_doc/clipseg +# title: CLIPSeg +# - local: model_doc/clvp +# title: CLVP +# - local: model_doc/data2vec +# title: Data2Vec +# - local: model_doc/deplot +# title: DePlot +# - local: model_doc/donut +# title: Donut +# - local: model_doc/flava +# title: FLAVA +# - local: model_doc/git +# title: GIT +# - local: model_doc/grounding-dino +# title: Grounding DINO +# - local: model_doc/groupvit +# title: GroupViT +# - local: model_doc/idefics +# title: IDEFICS +# - local: model_doc/idefics2 +# title: Idefics2 +# - local: model_doc/instructblip +# title: InstructBLIP +# - local: model_doc/kosmos-2 +# title: KOSMOS-2 +# - local: model_doc/layoutlm +# title: LayoutLM +# - local: model_doc/layoutlmv2 +# title: LayoutLMV2 +# - local: model_doc/layoutlmv3 +# title: LayoutLMV3 +# - local: model_doc/layoutxlm +# title: LayoutXLM +# - local: model_doc/lilt +# title: LiLT +# - local: model_doc/llava +# title: Llava +# - local: model_doc/llava_next +# title: LLaVA-NeXT +# - local: model_doc/lxmert +# title: LXMERT +# - local: model_doc/matcha +# title: MatCha +# - local: model_doc/mgp-str +# title: MGP-STR +# - local: model_doc/nougat +# title: Nougat +# - local: model_doc/oneformer +# title: OneFormer +# - local: model_doc/owlvit +# title: OWL-ViT +# - local: model_doc/owlv2 +# title: OWLv2 +# - local: model_doc/paligemma +# title: PaliGemma +# - local: model_doc/perceiver +# title: Perceiver +# - local: model_doc/pix2struct +# title: Pix2Struct +# - local: model_doc/sam +# title: Segment Anything +# - local: model_doc/siglip +# title: SigLIP +# - local: model_doc/speech-encoder-decoder +# title: Speech Encoder Decoder Models +# - local: model_doc/tapas +# title: TAPAS +# - local: model_doc/trocr +# title: TrOCR +# - local: model_doc/tvlt +# title: TVLT +# - local: model_doc/tvp +# title: TVP +# - local: model_doc/udop +# title: UDOP +# - local: model_doc/video_llava +# title: VideoLlava +# - local: model_doc/vilt +# title: ViLT +# - local: model_doc/vipllava +# title: VipLlava +# - local: model_doc/vision-encoder-decoder +# title: Vision Encoder Decoder Models +# - local: model_doc/vision-text-dual-encoder +# title: Vision Text Dual Encoder +# - local: model_doc/visual_bert +# title: VisualBERT +# - local: model_doc/xclip +# title: X-CLIP +# title: Multimodal models +# - isExpanded: false +# sections: +# - local: model_doc/decision_transformer +# title: محول القرار +# - local: model_doc/trajectory_transformer +# title: محول المسار +# title: نماذج التعلم التعزيزية +# - isExpanded: false +# sections: +# - local: model_doc/autoformer +# title: Autoformer +# - local: model_doc/informer +# title: Informer +# - local: model_doc/patchtsmixer +# title: PatchTSMixer +# - local: model_doc/patchtst +# title: PatchTST +# - local: model_doc/time_series_transformer +# title: محول السلاسل الزمنية +# title: نماذج السلاسل الزمنية +# - isExpanded: false +# sections: +# - local: model_doc/graphormer +# title: Graphormer +# title: نماذج الرسم البياني +# title: النماذج +# - sections: +# - local: internal/modeling_utils +# title: الطبقات المخصصة والمرافق +# - local: internal/pipelines_utils +# title: مرافق خطوط الأنابيب +# - local: internal/tokenization_utils +# title: مرافق مقسم النصوص +# - local: internal/trainer_utils +# title: مرافق المدرب +# - local: internal/generation_utils +# title: مرافق التوليد +# - local: internal/image_processing_utils +# title: مرافق معالجة الصور +# - local: internal/audio_utils +# title: مرافق معالجة الصوت +# - local: internal/file_utils +# title: مرافق عامة +# - local: internal/time_series_utils +# title: مرافق السلاسل الزمنية +# title: مساعدون داخليون +# title: API diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/accelerate.md b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/accelerate.md new file mode 100644 index 0000000000000000000000000000000000000000..486c1efe59af60c0cf524d9493f3d5ba34058b2a --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/accelerate.md @@ -0,0 +1,120 @@ +# التدريب الموزع باستخدام 🤗 Accelerate + + +مع تزايد حجم النماذج اللغوية، برز التوازي كأحد الاستراتيجيات لتدريب نماذج أكبر على أجهزة محدودة وتسريع عملية التدريب بمقدار كبير. أنشأنا في Hugging Face، قمنا بإنشاء مكتبة [ Accelerate](https://huggingface.co/docs/accelerate) لمساعدة المستخدمين على تدريب أي نموذج من Transformers بسهولة على أي نوع من الإعدادات الموزعة، سواء كان ذلك على عدة وحدات معالجة رسومات (GPUs) على جهاز واحد أو على عدة وحدات معالجة رسومات موزعة على عدة أجهزة. في هذا الدليل، تعلم كيفية تخصيص حلقة تدريب PyTorch الأصلية لتمكين التدريب في بيئة موزعة. + +## الإعداد + +ابدأ بتثبيت 🤗 Accelerate: + +```bash +pip install accelerate +``` + +ثم قم باستيراد وإنشاء كائن [`~accelerate.Accelerator`]. سيقوم [`~accelerate.Accelerator`] تلقائيًا باكتشاف نوع الإعداد الموزع الخاص بك وتهيئة جميع المكونات اللازمة للتدريب. لن تحتاج إلى وضع نموذجك على جهاز بشكل معين. + +```py +>>> from accelerate import Accelerator + +>>> accelerator = Accelerator() +``` + +## الاستعداد للتسريع + +الخطوة التالية هي تمرير جميع كائنات التدريب ذات الصلة إلى دالة الإعداد [`~accelerate.Accelerator.prepare`]. ويشمل ذلك DataLoaders للتدريب والتقييم، ونموذجًا ومُحَسِّنً المعاملات (optimizer): + +```py +>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( +... train_dataloader, eval_dataloader, model, optimizer +... ) +``` + +## الخلفي Backward + +الإضافة الأخيرة هي استبدال الدالة المعتادة `loss.backward()` في حلقة التدريب الخاصة بك بدالة [`~accelerate.Accelerator.backward`] في 🤗 Accelerate: + +```py +>>> for epoch in range(num_epochs): +... for batch in train_dataloader: +... outputs = model(**batch) +... loss = outputs.loss +... accelerator.backward(loss) + +... optimizer.step() +... lr_scheduler.step() +... optimizer.zero_grad() +... progress_bar.update(1) +``` + +كما يمكنك أن ترى في الكود التالي، فأنت بحاجة فقط إلى إضافة أربعة أسطر من الكود إلى حلقة التدريب الخاصة بك لتمكين التدريب الموزع! + +```diff ++ from accelerate import Accelerator + from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler + ++ accelerator = Accelerator() + + model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) + optimizer = AdamW(model.parameters(), lr=3e-5) + +- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") +- model.to(device) + ++ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare( ++ train_dataloader, eval_dataloader, model, optimizer ++ ) + + num_epochs = 3 + num_training_steps = num_epochs * len(train_dataloader) + lr_scheduler = get_scheduler( + "linear", + optimizer=optimizer, + num_warmup_steps=0, + num_training_steps=num_training_steps + ) + + progress_bar = tqdm(range(num_training_steps)) + + model.train() + for epoch in range(num_epochs): + for batch in train_dataloader: +- batch = {k: v.to(device) for k, v in batch.items()} + outputs = model(**batch) + loss = outputs.loss +- loss.backward() ++ accelerator.backward(loss) +optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + progress_bar.update(1) +``` + +## تدريب + +بمجرد إضافة أسطر الكود ذات الصلة، قم بتشغيل التدريب الخاص بك في أحد النصوص أو الدفاتر مثل Colaboratory. + +### التدريب باستخدام نص برمجي + +إذا كنت تشغل التدريب الخاص بك من نص برمجي، فقم بتشغيل الأمر التالي لإنشاء وحفظ ملف تكوين: + +```bash +accelerate config +``` + +ثم قم بتشغيل التدريب الخاص بك باستخدام: + +```bash +accelerate launch train.py +``` + +### التدريب باستخدام دفتر ملاحظات + +يمكن أيضًا تشغيل 🤗 Accelerate في دفاتر إذا كنت تخطط لاستخدام وحدات معالجة الرسوميات (TPUs) في Colaboratory. قم بتغليف كل الكود المسؤول عن التدريب في دالة، ومررها إلى [`~accelerate.notebook_launcher`]: + +```py +>>> from accelerate import notebook_launcher + +>>> notebook_launcher(training_function) +``` + +للحصول على مزيد من المعلومات حول 🤗 Accelerate وميزاته الغنية، يرجى الرجوع إلى [الوثائق](https://huggingface.co/docs/accelerate). \ No newline at end of file diff --git a/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/agents.md b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/agents.md new file mode 100644 index 0000000000000000000000000000000000000000..c7efd8f02f48c07471ea699b4fe57f685e3c8dfa --- /dev/null +++ b/tasks/tasksmith-e0f68dd4420c/environment/source/docs/source/ar/agents.md @@ -0,0 +1,539 @@ +# الوكلاء والأدوات + +[[open-in-colab]] + +### ما هو الوكيل؟ + +يمكن للنظم اللغوية الكبيرة (LLMs) التي تم تدريبها على أداء [نمذجة اللغة السببية](./tasks/language_modeling.) التعامل مع مجموعة واسعة من المهام، ولكنها غالبًا ما تواجه صعوبات في المهام الأساسية مثل المنطق والحساب والبحث. وعندما يتم استدعاؤها في مجالات لا تؤدي فيها أداءً جيدًا، فإنها غالبًا ما تفشل في توليد الإجابة التي نتوقعها منها. + +يتمثل أحد النهج للتغلب على هذا القصور في إنشاء "وكيل". + +الوكيل هو نظام يستخدم LLM كمحرك له، ولديه حق الوصول إلى وظائف تسمى "أدوات". + +هذه "الأدوات" هي وظائف لأداء مهمة، وتحتوي على جميع الأوصاف اللازمة للوكيل لاستخدامها بشكل صحيح. + +يمكن برمجة الوكيل للقيام بما يلي: +- وضع سلسلة من الإجراءات/الأدوات وتشغيلها جميعًا في نفس الوقت مثل [`CodeAgent`] على سبيل المثال +- التخطيط للاجراءات/الأدوات وتنفيذها واحدة تلو الأخرى والانتظار حتى انتهاء كل إجراء قبل إطلاق التالي مثل [`ReactJsonAgent`] على سبيل المثال + +### أنواع الوكلاء + +#### الوكيل البرمجي (Code agent) + +يتمتع هذا الوكيل يتبع خطوات محددة: أولًا، يخطط لسلسلة من الإجراءات التي يريد تنفيذها، ثم شفرة Python لتنفيذ جميع الإجراءات في نفس الوقت. وهو يتعامل بشكل أصلي مع أنواع مختلفة من المدخلات والمخرجات للأدوات التي يستخدمها، وبالتالي فهو الخيار الموصى به للمهام متعددة الوسائط. + +#### وكلاء التفاعل + +هذا هو الوكيل الذي يتم اللجوء إليه لحل مهام الاستدلال، حيث يجعل إطار ReAct ([Yao et al.، 2022](https://huggingface.co/papers/2210.03629)) من الكفاءة حقًا التفكير على أساس ملاحظاته السابقة. + +نقوم بتنفيذ إصدارين من ReactJsonAgent: +- [`ReactJsonAgent`] يقوم بتوليد استدعاءات الأدوات كـ JSON في إخراجها. +- [`ReactCodeAgent`] هو نوع جديد من ReactJsonAgent يقوم بتوليد استدعاءات أدواته كمقاطع من التعليمات البرمجية، والتي تعمل بشكل جيد حقًا مع LLMs التي تتمتع بأداء قوي في البرمجة. + +> [!TIP] +> اقرأ منشور المدونة [Open-source LLMs as LangChain Agents](https://huggingface.co/blog/open-source-llms-as-agents) لمعرفة المزيد عن وكيل ReAct. + +![إطار عمل وكيل ReAct](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open-source-llms-as-agents/ReAct.png) + +على سبيل المثال، إليك كيف يعمل وكيل ReAct Code طريقه من خلال السؤال التالي. + +```py3 +>>> agent.run( +... "How many more blocks (also denoted as layers) in BERT base encoder than the encoder from the architecture proposed in Attention is All You Need?", +... ) +=====New task===== +How many more blocks (also denoted as layers) in BERT base encoder than the encoder from the architecture proposed in Attention is All You Need? +====Agent is executing the code below: +bert_blocks = search(query="number of blocks in BERT base encoder") +print("BERT blocks:", bert_blocks) +==== +Print outputs: +BERT blocks: twelve encoder blocks + +====Agent is executing the code below: +attention_layer = search(query="number of layers in Attention is All You Need") +print("Attention layers:", attention_layer) +==== +Print outputs: +Attention layers: Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position- 2 Page 3 Figure 1: The Transformer - model architecture. + +====Agent is executing the code below: +bert_blocks = 12 +attention_layers = 6 +diff = bert_blocks - attention_layers +print("Difference in blocks:", diff) +final_answer(diff) +==== + +Print outputs: +Difference in blocks: 6 + +Final answer: 6 +``` + +### كيف يمكنني بناء وكيل؟ + +لتهيئة وكيل، تحتاج إلى هذه الوسائط: + +- نموذج لغوي كبير (LLM) يشكل المحرك الأساسي للوكيل. الوكيل نفسه ليس النموذج اللغوي، بل هو برنامج يستخدم النموذج اللغوي كمحرك له. +- موجه النظام (system prompt): هذه هي التعليمات التي يتم إعطاؤها للنموذج اللغوي لإنشاء مخرجاته. +- صندوق أدوات (toolbox) يختار الوكيل منه الأدوات لتنفيذها +- محلل (parser) لاستخراج الأدوات التي يجب استدعاؤها من مخرجات النموذج اللغوي LLM والأدوات التي يجب استخدامها + +عند تهيئة نظام الوكيل، يتم استخدام سمات الأداة لإنشاء وصف للأداة، ثم يتم دمجها في موجه النظام الخاص `system_prompt` للوكيل لإعلامه بالأدوات التي يمكنه استخدامها ولماذا. + +للبدء، يرجى تثبيت `agents` الإضافية لتثبيت جميع التبعيات الافتراضية. + +```bash +pip install transformers[agents] +``` + +قم ببناء محرك LLM الخاص بك من خلال تعريف طريقة `llm_engine` التي تقبل قائمة من [الرسائل](./chat_templating.) وتعيد النص. يجب أن تقبل هذه الدالة القابلة للاستدعاء أيضًا معامل `stop` يشير إلى متى يجب التوقف عن التوليد. + +```python +from huggingface_hub import login, InferenceClient + +login("") + +client = InferenceClient(model="meta-llama/Meta-Llama-3-70B-Instruct") + +def llm_engine(messages, stop_sequences=["Task"]) -> str: + response = client.chat_completion(messages, stop=stop_sequences, max_tokens=1000) + answer = response.choices[0].message.content + return answer +``` + +يمكنك استخدام أي طريقة `llm_engine` طالما أنها: +1. يتبع تنسيق [رسائل](./chat_templating.md) لإدخاله (`List [Dict [str، str]]`) ويعيد `str` +2. يتوقف عن توليد المخراجات من التسلسلات التي تم تمريرها في معامل `stop` + +أنت بحاجة أيضًا إلى معامل "الأدوات" الذي يقبل قائمة من "الأدوات". يمكنك توفير قائمة فارغة لـ "الأدوات"، ولكن استخدم صندوق الأدوات الافتراضي مع معامل اختياري `add_base_tools=True`. + +الآن يمكنك إنشاء وكيل، مثل [`CodeAgent`], وتشغيله. ولتسهيل الأمر، نقدم أيضًا فئة [`HfEngine`] التي تستخدم `huggingface_hub.InferenceClient` بشكل مخفى. + +```python +from transformers import CodeAgent, HfEngine + +llm_engine = HfEngine(model="meta-llama/Meta-Llama-3-70B-Instruct") +agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True) + +agent.run( + "Could you translate this sentence from French, say it out loud and return the audio.", + sentence="Où est la boulangerie la plus proche?", +) +``` + +هذه الميزة ستكون مفيدة في حالة الحاجة الملحة! يمكنك حتى ترك معامل `llm_engine` غير محدد، وسيتم إنشاء [`HfEngine`] بشكل تلقائي. + +```python +from transformers import CodeAgent + +agent = CodeAgent(tools=[], add_base_tools=True) + +agent.run( + "Could you translate this sentence from French, say it out loud and give me the audio.", + sentence="Où est la boulangerie la plus proche?", +) +``` + +لاحظ أننا استخدمنا معامل "sentence" إضافي: يمكنك تمرير النص كمعامل إضافي إلى النموذج. + +يمكنك أيضًا استخدام هذا للإشارة إلى مسار الملفات المحلية أو البعيدة للنموذج لاستخدامها: + +```py +from transformers import ReactCodeAgent + +agent = ReactCodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True) + +agent.run("Why does Mike not know many people in New York?", audio="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/recording.mp3") +``` + + +تم تحديد موجه النظام ومحلل المخرجات تلقائيًا، ولكن يمكنك فحصهما بسهولة عن طريق استدعاء `system_prompt_template` على وكيلك. + +```python +print(agent.system_prompt_template) +``` + +من المهم أن تشرح بأكبر قدر ممكن من الوضوح المهمة التي تريد تنفيذها. +كل عملية [`~Agent.run`] مستقلة، وبما أن الوكيل مدعوم من LLM، فقد تؤدي الاختلافات الطفيفة في موجهك إلى نتائج مختلفة تمامًا. +يمكنك أيضًا تشغيل وكيل بشكل متتالي لمهام مختلفة: في كل مرة يتم فيها إعادة تهيئة سمتي `agent.task` و`agent.logs`. + + +#### تنفيذ التعليمات البرمجية + +يقوم مفسر Python بتنفيذ التعليمات البرمجية على مجموعة من المدخلات التي يتم تمريرها جنبًا إلى جنب مع أدواتك. +يجب أن يكون هذا الأمر آمنًا لأن الوظائف الوحيدة التي يمكن استدعاؤها هي الأدوات التي قدمتها (خاصة إذا كانت أدوات من Hugging Face فقط) ووظيفة الطباعة، لذا فأنت مقيد بالفعل بما يمكن تنفيذه. + +مفسر Python لا يسمح أيضًا باستدعاء دوال بشكل افتراضي خارج قائمة آمنة، لذا فإن جميع الهجمات الأكثر وضوحًا لا ينبغي أن تكون مشكلة. +يمكنك أيضًا الإذن باستيرادات إضافية عن طريق تمرير الوحدات النمطية المصرح بها كقائمة من السلاسل في معامل `additional_authorized_imports` عند تهيئة [`ReactCodeAgent`] أو [`CodeAgent`]: + +```py +>>> from transformers import ReactCodeAgent + +>>> agent = ReactCodeAgent(tools=[], additional_authorized_imports=['requests', 'bs4']) +>>> agent.run("Could you get me the title of the page at url 'https://huggingface.co/blog'?") + +(...) +'Hugging Face – Blog' +``` + +سيتم إيقاف التنفيذ عند أي رمز يحاول تنفيذ عملية غير قانونية أو إذا كان هناك خطأ Python عادي في التعليمات البرمجية التي تم إنشاؤها بواسطة الوكيل. + +> [!WARNING] +> يمكن لـ LLM توليد شفرة برمجية عشوائية سيتم تنفيذها بعد ذلك: لا تقمب استدعاء أى دوال غير آمنة! + +### موجه النظام + +ينشئ الوكيل، أو بالأحرى LLM الذي يقود الوكيل، يولد مخرجات بناءً على موجه النظام. يمكن تخصيص موجه النظام وتصميمه للمهام المقصودة. على سبيل المثال، تحقق من موجه النظام لـ [`ReactCodeAgent`] (الإصدار أدناه مبسط قليلاً). + +```text +You will be given a task to solve as best you can. +You have access to the following tools: +<> + +To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences. + +At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task, then the tools that you want to use. +Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '/End code' sequence. +During each intermediate step, you can use 'print()' to save whatever important information you will then need. +These print outputs will then be available in the 'Observation:' field, for using this information as input for the next step. + +In the end you have to return a final answer using the `final_answer` tool. + +Here are a few examples using notional tools: +--- +{examples} + +Above example were using notional tools that might not exist for you. You only have access to those tools: +<> +You also can perform computations in the python code you generate. + +Always provide a 'Thought:' and a 'Code:\n```py' sequence ending with '```' sequence. You MUST provide at least the 'Code:' sequence to move forward. + +Remember to not perform too many operations in a single code block! You should split the task into intermediate code blocks. +Print results at the end of each step to save the intermediate results. Then use final_answer() to return the final result. + +Remember to make sure that variables you use are all defined. + +Now Begin! +``` + +يتضمن موجه النظام: +- *مقدمة* تشرح كيف يجب أن يتصرف الوكيل والأدوات التي يجب عليه استخدامها. +- وصف لجميع الأدوات التي يتم تحديدها بواسطة رمز `<>` الذي يتم استبداله ديناميكيًا في وقت التشغيل بالأدوات التي يحددها المستخدم أو يختارها. + - يأتي وصف الأداة من سمات الأداة، `name`، و`description`، و`inputs` و`output_type`، وقالب `jinja2` بسيط يمكنك تحسينه. +- شكل المخرج المتوقع. + +يمكنك تحسين موجه النظام، على سبيل المثال، عن طريق إضافة شرح لتنسيق المخرجات. + +للحصول على أقصى قدر من المرونة، يمكنك الكتابة فوق قالب موجه النظام بالكامل عن طريق تمرير موجه مخصص كمعامل إلى معلمة `system_prompt`. + +```python +from transformers import ReactJsonAgent +from transformers.agents import PythonInterpreterTool + +agent = ReactJsonAgent(tools=[PythonInterpreterTool()], system_prompt="{your_custom_prompt}") +``` + +> [!WARNING] +> يرجى التأكد من تحديد سلسلة `<>` في مكان ما في `template` حتى يكون الوكيل على علم +بالأدوات المتاحة. + + +### فحص تشغيل الوكيل + +فيما يلي بعض السمات المفيدة لفحص ما حدث بعد التشغيل: +- تخزن `agent.logs` سجلات مفصلة للوكيل. في كل خطوة من تشغيل الوكيل، يتم تخزين كل شيء في قاموس إلحاقه بـ `agent.logs`. +- تشغيل `agent.write_inner_memory_from_logs()` يخلق ذاكرة داخلية لسجلات الوكيل للنظام LLM لعرضها، كقائمة من رسائل الدردشة. تنتقل هذه الطريقة عبر كل خطوة من سجل الوكيل ولا تخزن سوى ما يهمها كرسالة: على سبيل المثال، سيحفظ موجه النظام والمهمة في رسائل منفصلة، ثم لكل خطوة سيخزن مخرج LLM كرسالة، ومخرج استدعاء الأداة كرسالة أخرى. استخدم هذا إذا كنت تريد عرضًا عامًا لما حدث - ولكن لن يتم نسخ كل سجل بواسطة هذه الطريقة. + +## الأدوات + +الأداة هي عبارة عن وظيفة أساسية يستخدمها الوكيل لتنفيذ مهمة محددة. + +يمكنك على سبيل المثال التحقق من [`PythonInterpreterTool`]: لديه اسم ووصف ووصف للمدخلات ونوع للمخرج، وطريقة `__call__` التي تقوم بتنفيذ المهمة المطلوبة. + +عند تهيئة الوكيل، يتم استخدام سمات الأداة لتوليد وصف للأداة يتم تضمينه في موجه النظام الخاص بالوكيل. يتيح هذا للوكيل معرفة الأدوات التي يمكنه استخدامها ولماذا. + +### صندوق الأدوات الافتراضي + +يأتي Transformers مع صندوق أدوات افتراضي لتمكين الوكلاء، والذي يمكنك إضافته إلى وكيلك عند التهيئة باستخدام معامل `add_base_tools = True`: + +- **الإجابة على أسئلة المستند**: الإجابة على سؤال حول المستند (مثل ملف PDF) بتنسيق صورة ([Donut](./model_doc/donut)) +- **الإجابة على أسئلة الصور**: الإجابة على سؤال حول صورة ([VILT](./model_doc/vilt)) +- **التحدث إلى النص**: قم بتفريغ الكلام إلى نص ([Whisper](./model_doc/whisper)) +- **النص إلى كلام**: تحويل النص إلى كلام ([SpeechT5](./model_doc/speecht5)) +- **الترجمة**: ترجمة جملة معينة من لغة المصدر إلى لغة الهدف. +- **مفسر كود Python**: تشغيل كود Python الذي تم إنشاؤه بواسطة LLM في بيئة آمنة. لن يتم إضافة هذه الأداة إلى [`ReactJsonAgent`] إلا إذا استخدمت `add_base_tools=True`، نظرًا لأن الأدوات المستندة إلى التعليمات البرمجية يمكنها بالفعل تنفيذ كود Python +لا تترجم النصوص الخاصة ولا الأكواد البرمجية ولا الروابط ولا رموز HTML وCSS: + +يمكنك استخدام أداة يدويًا عن طريق استدعاء دالة [`load_tool`] وتحديد مهمة لتنفيذها. + +```python +from transformers import load_tool + +tool = load_tool("text-to-speech") +audio = tool("This is a text to speech tool") +``` + +### إنشاء أداة جديدة + +يمكنك إنشاء أداتك الخاصة لتغطية حالات الاستخدام التي لا تغطيها الأدوات الافتراضية من Hugging Face. +على سبيل المثال، دعنا نقوم بإنشاء أداة تعرض النموذج الأكثر تنزيلًا لمهمة معينة من Hub. + +سوف نبدأ بالكود التالي. + +```python +from huggingface_hub import list_models + +task = "text-classification" + +model = next(iter(list_models(filter=task, sort="downloads", direction=-1))) +print(model.id) +``` + +يمكن تحويل هذه الشيفرة إلى فئة ترث من الفئة العليا [`Tool`]. + +تحتاج الأداة المخصصة إلى: + +- اسم `name`، والتي تمثل اسم الأداة نفسها. عادةً ما يصف الاسم وظيفتها. بما أن الكود يعيد النموذج الأكثر تنزيلًا لمهمة ما، فلنسمها `model_download_counter`. +- تستخدم خاصية `description` لملء موجه نظام الوكيل. +- خاصية `inputs`، والتي هي عبارة عن قاموس بمفاتيح "type" و"description". يحتوي على معلومات تساعد المفسر Python على اتخاذ خيارات مستنيرة بشأن المدخلات. +- خاصية `output_type`، والتي تحدد نوع المخرج. +- طريقة `forward` والتي تحتوي على الكود الذي سيتم تنفيذه للحصول على النتيجة النهائية. + +```python +from transformers import Tool +from huggingface_hub import list_models + +class HFModelDownloadsTool(Tool): + name = "model_download_counter" + description = ( + "This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub. " + "It returns the name of the checkpoint." + ) + + inputs = { + "task": { + "type": "text", + "description": "the task category (such as text-classification, depth-estimation, etc)", + } + } + output_type = "text" + + def forward(self, task: str): + model = next(iter(list_models(filter=task, sort="downloads", direction=-1))) + return model.id +``` + +الآن بعد أن أصبحت فئة `HfModelDownloadsTool` المخصصة جاهزة، يمكنك حفظها في ملف باسم `model_downloads.py` واستيرادها للاستخدام. + +```python +from model_downloads import HFModelDownloadsTool + +tool = HFModelDownloadsTool() +``` + +يمكنك أيضًا مشاركة أداتك المخصصة في Hub عن طريق استدعاء [`~Tool.push_to_hub`] على الأداة. تأكد من أنك قمت بإنشاء مستودع لها على Hub وأنك تستخدم رمز وصول للقراءة. + +```python +tool.push_to_hub("{your_username}/hf-model-downloads") +``` + +قم بتحميل الأداة باستخدام دالة [`~Tool.load_tool`] ومررها إلى معلمة `tools` في الوكيل الخاص بك. + +```python +from transformers import load_tool, CodeAgent + +model_download_tool = load_tool("m-ric/hf-model-downloads") +agent = CodeAgent(tools=[model_download_tool], llm_engine=llm_engine) +agent.run( + "Can you give me the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?" +) +``` + +ستحصل على ما يلي: + +```text +======== New task ======== +Can you give me the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub? +==== Agent is executing the code below: +most_downloaded_model = model_download_counter(task="text-to-video") +print(f"The most downloaded model for the 'text-to-video' task is {most_downloaded_model}.") +==== +``` + +والناتج: + +`"النموذج الأكثر تنزيلًا لمهمة `text-to-video` هو ByteDance/AnimateDiff-Lightning."` + +### إدارة صندوق أدوات الوكيل الخاص بك + +إذا كنت قد قمت بتهيئة وكيل، فمن غير الملائم إعادة تهيئته من البداية لإضافة أداة جديدة ترغب في استخدامها. باستخدام مكتبة Transformers، يمكنك إدارة صندوق أدوات الوكيل بإضافة أو استبدال أداة موجودة. + +دعنا نضيف الأداة `model_download_tool` إلى وكيل تم تهيئته مسبقًا باستخدام صندوق الأدوات الافتراضي. + +```python +from transformers import CodeAgent + +agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True) +agent.toolbox.add_tool(model_download_tool) +``` + +الآن يمكننا الاستفادة من الأداة الجديدة وأداة تحويل النص إلى كلام السابقة: + +```python + agent.run( + "Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub and return the audio?" + ) +``` + +| **Audio** | +|------------------------------------------------------------------------------------------------------------------------------------------------------| +|